Best LLM for Structured Data Extraction in 2026
For structured data extraction, Muse Spark 1.3 Contributor is our pick: $0.12/M tokens on a Structured extraction batch workload, 1.0M context, graded 100/100 across 1 run.
Extraction is a high-volume, low-margin-for-error task: you need valid, correctly-typed JSON on the first try, every time, at a price that survives being called hundreds of thousands of times a month.
Quick answer: What is the best LLM for structured data extraction?
Muse Spark 1.3 Contributor, from Meta, is the best fit for structured data extraction at $0.12 per million task tokens on a Structured extraction batch workload, with a 1.0M-token context window. No cheaper value pick beats it for this task.
What evidence supports the Structured Data Extraction recommendation?
Graded on 14 of 49 eligible models, run 2026-06-16. Full prompts, verbatim outputs, and grading notes below.
Extracting Structured Data
A structured-output task: read one sentence and return strict JSON with a string name, numeric price, and boolean stock flag — no markdown, no prose.
| # | Model | Accuracy ↓ | Speed ↕ | Cost ↕ | Output |
|---|---|---|---|---|---|
| 1 | GPT-5.4 NanoOpenAI · gpt-5.4-nano | 100★ | 49.2 t/s488 ms | $0.0000456$1.25/M out | |
Agent grade — 100/100. Perfect: compact valid JSON, full product name, correct number and boolean types, no markdown or prose. Verbatim output (78 in / 24 out tokens, 488 ms, $0.0000456) {"name":"DeskPro X1 monitor stand","price":49.99,"in_stock":true} | |||||
| 2 | Muse Spark 1.3 ContributorMeta · muse-spark-1.3-contributor | 100★ | 207.1 t/s3704 ms | $0.000161$0.2/M out | |
Agent grade — 100/100. Valid, compact JSON; full product name; correct number and boolean types; no markdown or prose. Same reasoning tax as the other two tasks — most of the 767 output tokens were hidden chain-of-thought. Verbatim output (79 in / 767 out tokens, 3704 ms, $0.000161) {"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 3 | Gemini 3.1 Flash LiteGoogle · gemini-3.1-flash-lite | 99 | 75.5 t/s477 ms | $0.0000727$1.5/M out | |
Agent grade — 99/100. Valid JSON, full name, correct types, no markdown — just pretty-printed rather than compact. Verbatim output (75 in / 36 out tokens, 477 ms, $0.0000727) {
"name": "DeskPro X1 monitor stand",
"price": 49.99,
"in_stock": true
} | |||||
| 4 | Llama 3.3 70BGroq · llama-3.3-70b | 99 | 151.2 t/s172 ms | $0.0000837$0.79/M out | |
Agent grade — 99/100. Valid compact JSON, full name, correct types, no markdown or prose. Fastest run in this test. Verbatim output (107 in / 26 out tokens, 172 ms, $0.0000837) {"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 5 | Llama 3.1 8BGroq · llama-3.1-8b | 99 | 87.8 t/s296 ms | $0.0000074$0.08/M out | |
Agent grade — 99/100. Valid compact JSON, full name, correct types, no markdown — and the cheapest run here. Verbatim output (107 in / 26 out tokens, 296 ms, $0.0000074) {"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 6 | Mistral Medium 3Mistral · mistral-medium | 99 | 35.9 t/s808 ms | $0.0000944$2/M out | |
Agent grade — 99/100. Valid compact JSON, full name, correct types, no markdown or leakage. Verbatim output (91 in / 29 out tokens, 808 ms, $0.0000944) {"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 7 | Mistral Small 3.1Mistral · mistral-small | 99 | 54.7 t/s530 ms | $0.000031$0.6/M out | |
Agent grade — 99/100. Valid compact JSON, full name, correct types — identical quality to Medium for far less. Verbatim output (91 in / 29 out tokens, 530 ms, $0.000031) {"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 8 | CodestralMistral · codestral | 99 | 61.7 t/s470 ms | $0.0000498$0.9/M out | |
Agent grade — 99/100. Valid compact JSON, full name, correct types, no markdown or prose. Verbatim output (79 in / 29 out tokens, 470 ms, $0.0000498) {"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 9 | Amazon Nova MicroAmazon · nova-micro | 99 | 89.5 t/s380 ms | $0.0000074$0.14/M out | |
Agent grade — 99/100. Valid JSON, full name, correct types, no markdown or prose — only pretty-printed rather than compact. Verbatim output (76 in / 34 out tokens, 380 ms, $0.0000074) {
"name": "DeskPro X1 monitor stand",
"price": 49.99,
"in_stock": true
} | |||||
| 10 | Llama 4 ScoutGroq · llama-4-scout | 85 | 100 t/s350 ms | $0.0000209$0.34/M out | |
Agent grade — 85/100. Correct values and full name, but wrapped the JSON in a ```json markdown fence after being told "no markdown". Verbatim output (82 in / 35 out tokens, 350 ms, $0.0000209) ```json
{
"name": "DeskPro X1 monitor stand",
"price": 49.99,
"in_stock": true
}
``` | |||||
| 11 | Ministral 8BMistral · ministral-8b | 85 | 63.4 t/s599 ms | $0.0000175$0.15/M out | |
Agent grade — 85/100. Correct values and full name, but wrapped the output in a ```json markdown fence against instructions. Verbatim output (79 in / 38 out tokens, 599 ms, $0.0000175) ```json
{
"name": "DeskPro X1 monitor stand",
"price": 49.99,
"in_stock": true
}
``` | |||||
| 12 | Amazon Nova LiteAmazon · nova-lite | 85 | 111.8 t/s340 ms | $0.0000137$0.24/M out | |
Agent grade — 85/100. Correct values and full name, but wrapped the JSON in a ```json markdown fence after being told "no markdown". Verbatim output (76 in / 38 out tokens, 340 ms, $0.0000137) ```json
{
"name": "DeskPro X1 monitor stand",
"price": 49.99,
"in_stock": true
}
``` | |||||
| 13 | DeepSeek V4 FlashDeepSeek · deepseek-v4-flash | 82 | 60.5 t/s2033 ms | $0.0000455$0.28/M out | |
Agent grade — 82/100. Correct JSON with full name and proper types; docked only for the leaked <think> preamble. Verbatim output (79 in / 123 out tokens, 2033 ms, $0.0000455) <think>
We need to extract product details: name, price (number), in_stock. Sentence: "The DeskPro X1 monitor stand normally retails for $49.99 and is currently available to ship."
Name: "DeskPro X1 monitor stand" (should be string)
Price: $49.99 -> 49.99 as number
In_stock: "currently available to ship" implies it's in stock, so true.
Return JSON only.
</think>
{"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 14 | DeepSeek V4 ProDeepSeek · deepseek-v4-pro | 82 | 85.1 t/s2104 ms | $0.00019$0.87/M out | |
Agent grade — 82/100. Correct, complete JSON; the leaked <think> block is the only issue. Verbatim output (79 in / 179 out tokens, 2104 ms, $0.00019) <think>
We are asked to extract product details from the sentence: "The DeskPro X1 monitor stand normally retails for $49.99 and is currently available to ship."
We need to return ONLY a valid JSON object with keys: "name" (string), "price" (number), "in_stock" (boolean).
From the sentence:
- Name: "DeskPro X1 monitor stand"
- Price: $49.99 -> number 49.99
- In stock: "currently available to ship" implies it is in stock, so true.
So JSON: {"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true}
Make sure no extra text.
</think>
{"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 15 | Grok 4.3xAI · grok-4.3 | 80 | 26.3 t/s2819 ms | $0.000286$2.5/M out | |
Agent grade — 80/100. Valid JSON with the full name and correct types, but a leaked <think> block violates "return ONLY a valid JSON object". Verbatim output (81 in / 74 out tokens, 2819 ms, $0.000286) <think>
The task is: Extract the product details from the sentence and return ONLY a valid JSON object with exactly these keys: "name" (string), "price" (number), "in_stock" (boolean). Do not include any expl...
</think>
{
"name": "DeskPro X1 monitor stand",
"price": 49.99,
"in_stock": true
} | |||||
| 16 | GPT-OSS 120BGroq · gpt-oss-120b | 80 | 292.4 t/s277 ms | $0.00007$0.6/M out | |
Agent grade — 80/100. Final JSON is correct and complete, but a <think> block leaked in despite the "no explanation" instruction. Verbatim output (143 in / 81 out tokens, 277 ms, $0.00007) <think>
We need to output JSON with keys name, price, in_stock. Extract details: name: "DeskPro X1 monitor stand". price: 49.99. in_stock: true (available to ship). Return only JSON, no markdown.
</think>
{"name":"DeskPro X1 monitor stand","price":49.99,"in_stock":true} | |||||
| 17 | GPT-OSS 20BGroq · gpt-oss-20b | 80 | 478.1 t/s343 ms | $0.0000599$0.3/M out | |
Agent grade — 80/100. Correct JSON with the full name, but preceded by a leaked <think> reasoning block. Verbatim output (143 in / 164 out tokens, 343 ms, $0.0000599) <think>
The user wants to extract product details: name: "DeskPro X1 monitor stand", price: 49.99, in_stock: true. The sentence: "The DeskPro X1 monitor stand normally retails for $49.99 and is currently available to ship." The name: "DeskPro X1 monitor stand". Price: 49.99. In_stock: currently available to ship => in stock: true. JSON with keys: name, price, in_stock.
Return only JSON object, no explanation, no markdown. So just:
{"name":"DeskPro X1 monitor stand","price":49.99,"in_stock":true}
</think>
{"name":"DeskPro X1 monitor stand","price":49.99,"in_stock":true} | |||||
| 18 | Qwen 3 32BGroq · qwen3-32b | 75 | 428.6 t/s784 ms | $0.000222$0.59/M out | |
Agent grade — 75/100. Final JSON is correct, but buried under a ~340-token leaked <think> monologue — the opposite of "JSON only". Verbatim output (82 in / 336 out tokens, 784 ms, $0.000222) <think>
Okay, let me try to figure this out. The user wants me to extract product details from a sentence and put them into aJSON object with specific keys: name, price, and in_stock. The sentence given is "The DeskPro X1 monitor stand normally retails for $49.99 and is currently available to ship."
First, I need to identify the product name. The sentence starts with "The DeskPro X1 monitor stand..." so the name is probably "DeskPro X1 monitor stand". I should check if there's any other part of the name, but "DeskPro X1" seems like the model, and "monitor stand" is the product type. So the name key should be that full phrase.
Next, the price is mentioned as "$49.99". The key requires a number, so I need to remove the dollar sign and convert it to a numerical value. That would be 49.99.
For the in_stock boolean, the sentence says "is currently available to ship". If it's available to ship, that likely means it's in stock. So in_stock should be true.
I need to make sure there are only those three keys and that the JSON is valid. Let me double-check each part. The name is correct, price is a number without the currency symbol, and in_stock is a boolean. No other keys are needed. Alright, putting that all together into a JSON object with the exact keys.
</think>
{"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true} | |||||
| 19 | GLM 4.7 (Cerebras)Cerebras · cerebras-glm-4.7 | 73 | 562.9 t/s723 ms | $0.001292$2.75/M out | |
Agent grade — 73/100. The final JSON is correct, but the model exposed a ~400-token multi-step <think> process despite "no explanation". Verbatim output (77 in / 407 out tokens, 723 ms, $0.001292) <think>
1. **Analyze the Request:**
* Input sentence: "The DeskPro X1 monitor stand normally retails for $49.99 and is currently available to ship."
* Task: Extract product details.
* Output format: Valid JSON object only.
* Required keys: "name" (string), "price" (number), "in_stock" (boolean).
* Constraint: No explanation, no markdown code blocks (just the raw JSON).
2. **Analyze the Sentence to Extract Data:**
* *Name:* "The DeskPro X1 monitor stand". I should probably clean this up slightly to just the product name, or keep it as is. "DeskPro X1 monitor stand" seems most appropriate. Let's stick to the core product name.
* *Price:* "$49.99". The key requires a number. So, 49.99.
* *Stock Status:* "currently available to ship". This implies it is in stock. So, true.
3. **Construct the JSON Object:**
* `name`: "DeskPro X1 monitor stand"
* `price`: 49.99
* `in_stock`: true
4. **Verify against Constraints:**
* Valid JSON? Yes.
* Keys present? "name", "price", "in_stock". Yes.
* Types correct? String, Number, Boolean. Yes.
* No explanation? Yes.
* No markdown? Yes (I need to ensure I don't wrap it in backticks).
5. **Final Output Generation:**
{"name": "DeskPro X1 monitor stand", "price": 49.99, "in_stock": true}
</think>
{
"name": "DeskPro X1 monitor stand",
"price": 49.99,
"in_stock": true
} | |||||
| 20 | GPT-OSS 120B (Cerebras)Cerebras · cerebras-gpt-oss-120b | 72 | 557.6 t/s165 ms | $0.000118$0.75/M out | |
Agent grade — 72/100. Leaked a <think> block AND kept the leading "The" in the name ("The DeskPro X1 monitor stand"), so the extraction is slightly off. Verbatim output (139 in / 92 out tokens, 165 ms, $0.000118) <think>
We need to extract product details: name, price, in_stock. Name is "The DeskPro X1 monitor stand". Price is 49.99. in_stock: currently available to ship => true. Return JSON only, no markdown. Ensure keys exact. Provide number, boolean, string.
</think>
{"name":"The DeskPro X1 monitor stand","price":49.99,"in_stock":true} | |||||
Reproducible Structured Data Extraction evidence and decision rubric
| Test / run | Prompt and verification | Hard rule |
|---|---|---|
| Code Snippet | exact prompt + 20 recorded runs | Correct iterative algorithm and code-only output |
| Hard Algorithm | exact prompt + 4 recorded runs | 5,000-case harness; O(log n) partition and correct edge cases |
Run dates: budget suite 2026-06-16T20:31:30.728Z; premium suite 2026-06-21T00:00:00.000Z. Results are not a claim about every repository or prompt.
Two-test rubric, failure analysis, and task-shaped ranking
| Model | Accuracy | Latency | Output tokens | Run cost | Failure / qualification note |
|---|---|---|---|---|---|
| GPT-5.4 Pro | 100/100 | 206116 ms | 548 | $0.103 | Passes all 5,000+ randomized cases and every edge case; genuine O(log(min(m,n))) partition search, returns a float, code-only, and explicitly raises on two empty lists. Correct — but the slowest run by far (over three minutes), and now that real usage is reported, comfortably the most expensive. |
| Claude Opus 4.8 | 100/100 | 3790 ms | 382 | $0.011 | Passes all 5,000+ randomized cases and every edge case; genuine O(log(min(m,n))) partition, float return, code-only. The fastest correct solution in this task. |
| GLM 5.2 (Max) | 100/100 | 47713 ms | 2148 | $0.010 | Passes all 5,000+ randomized cases and every edge case with a genuine O(log) partition and float return. The <think> block ahead of the code is GLM's reasoning channel surfaced by our gateway, not reasoning dumped into the answer — GLM's actual content is the clean code block — so it scores level with the other correct solutions, as the cheapest of them. |
| Gemini 3.1 Pro | 99/100 | 17413 ms | 320 | $0.004 | Passes all tests with a clean, minimal O(log) partition and float return, code-only. Docked one point only because two empty lists yield NaN rather than an explicit guard (not required by the prompt). |
Availability caveat: short code tests do not establish repository-scale debugging, multi-file tool use, or agent reliability. The fastest acceptable verdict must therefore clear the correctness rule before speed is considered.
Task-shaped cost ranking (20,000 tasks/month)
| Rank | Model | Effective monthly | Measured verbosity |
|---|---|---|---|
| 1 | Amazon Nova Micro | $18.26 | 0.76× |
| 2 | Amazon Nova Lite | $32.74 | 0.91× |
| 3 | GPT-5 Nano | $36.00 | Unavailable; neutral fallback |
| 4 | Gemini 2.5 Flash Lite | $56.00 | Unavailable; neutral fallback |
| 5 | GPT-OSS 20B | $65.16 | 2.93× |
| 6 | Ministral 8B | $65.58 | 0.93× |
Verified 2026-08-08. full prompt/run evidence →
Try these models for Structured Data Extraction →Volume, requirement-gate, and pricing cross-check for Structured Data Extraction
Monthly spend ladder at Structured extraction batch shape
| Calls / month | Overall pick monthly | Budget pick monthly | Overall − budget delta |
|---|---|---|---|
| 50,000 | $6.00 | $6.00 | $0.0000 |
| 100,000 | $12.00 | $12.00 | $0.0000 |
| 200,000 | $24.00 | $24.00 | $0.0000 |
| 500,000 | $60.00 | $60.00 | $0.0000 |
Monthly cost = task price/M × (800 input + 200 output tokens) × calls ÷ 1,000,000, at 0.5×, 1×, 2×, 5× the published 100,000-call/month baseline.
Requirement-gate margin for the picked models
| Model | Requirement | Measured value | Margin / result |
|---|---|---|---|
| — | No hard requirement filter for this task | requires: {} | All qualified candidates pass by default |
Requirement gates are hard filters, not down-ranking: a model failing any row here is excluded from Structured Data Extraction candidates entirely, regardless of price or speed.
Pricing-page cross-link for each pick
| Model | Task-weighted $/M | Monthly at published volume | Measured throughput |
|---|---|---|---|
| Muse Spark 1.3 Contributor | $0.12 | $12.00 | Unavailable |
| GPT-OSS 120B (Cerebras) | $0.43 | $43.00 | 2450 tok/s |
| Gemini 3.1 Pro | $4.00 | $400.00 | 55 tok/s |
Evidence coverage: 14 of 49 candidates have a graded run, run on 2026-06-16T20:31:30.728Z.
Test the Structured Data Extraction picks side by side →Verified 2026-08-08. "Unavailable" means no compatible dated evidence was found for that field; it is never treated as zero. Dated source · Full ranking and rubric
Batch 10 extraction decision depth
1. Schema-growth budget
| Fields | Declared token shape | Cost / call | Cost / million extracted fields |
|---|---|---|---|
| 5 | 860 input + 240 output | $0.0001 | $26.80 |
| 25 | 1,100 input + 400 output | $0.0002 | $7.60 |
| 100 | 2,000 input + 1,000 output | $0.0004 | $4.00 |
Formula: cost = exact model input + output token rates from MODEL_PRICING. Cost / million extracted fields = (Cost / call ÷ fields) × 1,000,000. Per-field assumptions are visible scenario inputs: 12 input tokens and 8 output tokens per field; they are not provider facts.
2. Validation and repair paths
| Path | Calls | Decision formula / evidence boundary |
|---|---|---|
| First-pass parse | 1 call | Observed parse result only; no probability inferred |
| One model retry | 2 calls | 2 × API bill; retry rate is User-supplied |
| Human review | 1 call + review | API bill + (review minutes × hourly rate ÷ 60); both inputs User-supplied |
The single graded extraction run is one observed pass/fail/score outcome, not a Bernoulli probability. No malformed-record rate is inferred.
3. Graded-score floor router
| Floor | Measured eligible | Cheapest measured | Fastest measured |
|---|---|---|---|
| 70/100 | 14 | Amazon Nova Micro | GPT-OSS 120B (Cerebras) |
| 80/100 | 12 | Amazon Nova Micro | GPT-OSS 20B |
| 90/100 | 5 | Amazon Nova Micro | Amazon Nova Micro |
| 95/100 | 5 | Amazon Nova Micro | Amazon Nova Micro |
Models without a dated graded score are excluded from the floor, not assigned zero.
Verified 2026-08-08. Luna is the data owner. “Unavailable” means no compatible dated evidence was found; it is never zero, an estimate, or a guessed policy. Stale-data behavior: dated registry values are snapshots. If a source is older than the page verification date, or a provider changes its policy/pricing/model, re-verify before production use; unknown values remain Unavailable. Dated registry source · Run this scenario yourself →
Batch 59 · server-rendered evidence boards · verified 2026-09-07
Intent answer: Structured data extraction demands 100% valid JSON, strict schema adherence, zero field hallucinations, and low latency. Benchmarked accuracy and token economics identify the top models for production ETL and extraction. Verified 2026-09-07.
Demand evidence: Qualitative demand: dedicated JSON extraction benchmarks reviewed 2026-09-07; exact US monthly volume is unavailable.
Scope boundary: Compare and rank top AI models for structured JSON data extraction based on schema compliance, field accuracy, refusal rates, and cost efficiency. Exact joins required; unresolved joins render Unavailable.
Structured extraction schema conformance frontier
Deterministic formula / rule: conformance_score = (valid_json_count + exact_keys_match) / total_runs * 100; strict validator.
Boundary: Owns JSON schema compliance evaluation across models.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch59-task-extraction-m1-r1complex nested schema with arrays | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$complex nested schema with arrays; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m1-r2strict regex field constraints | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$strict regex field constraints; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m1-r3unstructured invoice to table | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$unstructured invoice to table; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m1-r4noisy OCR text extraction | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$noisy OCR text extraction; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m1-r5multilingual receipt extraction | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$multilingual receipt extraction; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m1-r6unsupported schema property | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$unsupported schema property; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07 | Unavailable — exact task-extraction evidence join is not closed for "unsupported schema property" | FAIL CLOSED — manual, probe, or source evidence required |
First-party citation: All AI Ask model specifications. Verified 2026-09-07; missing or conflicting joins fail closed.
Extraction cost-per-thousand-records receipt
Deterministic formula / rule: cost_per_1k = 1000 * (doc_input_tokens * rate_in + schema_output_tokens * rate_out); verbosity adjusted.
Boundary: Owns extraction unit economics across models.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch59-task-extraction-m2-r11-page invoice (800 in / 150 out) | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$1-page invoice (800 in / 150 out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m2-r25-page contract (4K in / 500 out) | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$5-page contract (4K in / 500 out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m2-r3medical record summary (8K in / 800 out) | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$medical record summary (8K in / 800 out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m2-r4financial prospectus table (15K in / 1.5K out) | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$financial prospectus table (15K in / 1.5K out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m2-r5batch classification (200 in / 20 out) | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$batch classification (200 in / 20 out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m2-r6unresolved token count | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$unresolved token count; document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07 | Unavailable — exact task-extraction evidence join is not closed for "unresolved token count" | FAIL CLOSED — manual, probe, or source evidence required |
First-party citation: All AI Ask pricing registry and formulas. Verified 2026-09-07; missing or conflicting joins fail closed.
Extraction failure recovery and fallback matrix
Deterministic formula / rule: fallback_route = (validation_failed) ? retry_with_higher_tier_model : accept_output; containment rule.
Boundary: Owns production error recovery and model routing.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch59-task-extraction-m3-r1malformed JSON syntax error | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$malformed JSON syntax error; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07 | Unavailable — exact task-extraction evidence join is not closed for "malformed JSON syntax error" | FAIL CLOSED — manual, probe, or source evidence required |
batch59-task-extraction-m3-r2missing mandatory property | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$missing mandatory property; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07 | Unavailable — exact task-extraction evidence join is not closed for "missing mandatory property" | FAIL CLOSED — manual, probe, or source evidence required |
batch59-task-extraction-m3-r3hallucinated extra property | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$hallucinated extra property; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m3-r4schema validation timeout | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$schema validation timeout; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m3-r5provider rate limit 429 | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$provider rate limit 429; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-extraction-m3-r6unrecoverable extraction defect | route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$unrecoverable extraction defect; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07 | Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
First-party citation: All AI Ask measured speed dataset. Verified 2026-09-07; missing or conflicting joins fail closed.
Method and limitations: this board exposes deterministic rules, first-party citations, and dated evidence identities. It does not invent volume, coverage, entitlement, retention, residency, quota, capacity, feature support, quality, price, reliability, or legal conclusions. Run the task-extraction evidence flow →
Which models rank highest for Structured Data Extraction?
"Fit" is a requirements match, not a quality benchmark — it combines price, measured speed, context window, and (where we have run it) graded accuracy on this task. Formula below.
| # | Model | Provider | Fit | Evidence | Task price/M | Tokens/sec | Context | Scored on |
|---|---|---|---|---|---|---|---|---|
| 1 | Muse Spark 1.3 Contributor | Meta | 93 | 100/1 | $0.12 | — | 1.0M | price, context, evidence |
| 2 | Gemini 2.5 Flash LiteLegacy | 81 | — | $0.16 | — | 1M | price, context | |
| 3 | Amazon Nova Micro | Amazon | 75 | 99/1 | $0.06 | 168 | 128K | price, context, speed, evidence |
| 4 | Mistral Small 3.1 | Mistral | 71 | 99/1 | $0.24 | 121 | 256K | price, context, speed, evidence |
| 5 | Amazon Nova Lite | Amazon | 69 | 85/1 | $0.10 | 108 | 300K | price, context, speed, evidence |
| 6 | GPT-OSS 20B | Groq | 69 | 80/1 | $0.12 | 1120 | 131K | price, context, speed, evidence |
| 7 | Codestral | Mistral | 68 | 99/1 | $0.42 | 118 | 256K | price, context, speed, evidence |
| 8 | GPT-OSS 120B (Cerebras) | Cerebras | 68 | 72/1 | $0.43 | 2450 | 131K | price, context, speed, evidence |
What will Structured Data Extraction cost?
At 100,000 structured extraction batch calls/month:
| Model | Task price/M | Est. monthly cost |
|---|---|---|
| Muse Spark 1.3 Contributor | $0.12 | $12.00 |
| Gemini 2.5 Flash Lite | $0.16 | $16.00 |
| Amazon Nova Micro | $0.06 | $5.60 |
How is the best LLM for Structured Data Extraction ranked?
Weights: evidence 45%, price 30%, speed 15%, context 10%.
Requirements: none — every current model is eligible. 49 models eligible.
Price and context sub-scores are min-max normalised (log-scaled) within this task's eligible set only. Speed uses measured tokens/sec only — estimated rows are excluded. A model missing a measurement is never scored as zero: its weight is redistributed across the components we do have, and "Scored on" in the table above shows exactly which ones.
Prices verified 2026-08-08, accuracy graded 2026-06-16.
What related resources help with Structured Data Extraction?
Where can you find evidence and costs for Structured Data Extraction?
What are common questions about the best LLM for Structured Data Extraction?
Why does JSON validity matter more than usual here?
A malformed response breaks a downstream parser, not just a human reader — our grading penalizes markdown fences and stray prose as hard as a wrong value.
Do I need a reasoning model for extraction?
No — extraction is pattern-matching against a fixed schema, and reasoning modes mostly add latency and cost here without improving accuracy.
What if my schema is more complex than the test?
Treat this ranking as a starting shortlist, then validate against your own schema — extraction accuracy degrades with nested and ambiguous fields in ways a single-object test cannot fully predict.
