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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.

Verdict: Because this runs at volume, price matters almost as much as correctness — a model that is 2 points more accurate but 5x the cost rarely wins the total-cost argument.

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.

Verified 2026-08-08
Best overall
Muse Spark 1.3 Contributor
Meta · $0.12/M
Fit 93/100 — the top requirements match for this task.
Best value
Muse Spark 1.3 Contributor
Meta · $0.12/M
The strongest fit among budget and mid-tier priced models.
Fastest
GPT-OSS 120B (Cerebras)
Cerebras · $0.43/M
2450 tokens/sec measured.
Longest context
Gemini 3.1 Pro
Google · $4.00/M
2M token context window.

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.

Click Accuracy, Speed, or Cost to re-sort. Click any row to read the full model output.
Per-model results: accuracy, speed (tokens/sec), provider cost (USD), and the full prompt-vs-output for each model on this task.
#ModelAccuracy Speed Cost Output
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}
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}
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
}
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}
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}
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}
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}
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}
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
}
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
}
```
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
}
```
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
}
```
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}
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}
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
}
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}
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}
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}
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
}
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}
Full test output, prompt, and grading notes →

Reproducible Structured Data Extraction evidence and decision rubric

Test / runPrompt and verificationHard rule
Code Snippetexact prompt + 20 recorded runsCorrect iterative algorithm and code-only output
Hard Algorithmexact prompt + 4 recorded runs5,000-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

ModelAccuracyLatencyOutput tokensRun costFailure / qualification note
GPT-5.4 Pro100/100206116 ms548$0.103Passes 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 4.8100/1003790 ms382$0.011Passes 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)100/10047713 ms2148$0.010Passes 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 Pro99/10017413 ms320$0.004Passes 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)

RankModelEffective monthlyMeasured verbosity
1Amazon Nova Micro$18.260.76×
2Amazon Nova Lite$32.740.91×
3GPT-5 Nano$36.00Unavailable; neutral fallback
4Gemini 2.5 Flash Lite$56.00Unavailable; neutral fallback
5GPT-OSS 20B$65.162.93×
6Ministral 8B$65.580.93×

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 / monthOverall pick monthlyBudget pick monthlyOverall − 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

ModelRequirementMeasured valueMargin / result
No hard requirement filter for this taskrequires: {}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

ModelTask-weighted $/MMonthly at published volumeMeasured throughput
Muse Spark 1.3 Contributor$0.12$12.00Unavailable
GPT-OSS 120B (Cerebras)$0.43$43.002450 tok/s
Gemini 3.1 Pro$4.00$400.0055 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

FieldsDeclared token shapeCost / callCost / million extracted fields
5860 input + 240 output$0.0001$26.80
251,100 input + 400 output$0.0002$7.60
1002,000 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

PathCallsDecision formula / evidence boundary
First-pass parse1 callObserved parse result only; no probability inferred
One model retry2 calls2 × API bill; retry rate is User-supplied
Human review1 call + reviewAPI 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

FloorMeasured eligibleCheapest measuredFastest measured
70/10014Amazon Nova MicroGPT-OSS 120B (Cerebras)
80/10012Amazon Nova MicroGPT-OSS 20B
90/1005Amazon Nova MicroAmazon Nova Micro
95/1005Amazon Nova MicroAmazon 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 IDExact identity and evidence fieldsResultState
batch59-task-extraction-m1-r1
complex nested schema with arrays
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$complex nested schema with arrays; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 1 rule is reproducible but no production observation is claimed
batch59-task-extraction-m1-r2
strict regex field constraints
route=$/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=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 1 rule is reproducible but no production observation is claimed
batch59-task-extraction-m1-r3
unstructured invoice to table
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$unstructured invoice to table; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 1 rule is reproducible but no production observation is claimed
batch59-task-extraction-m1-r4
noisy OCR text extraction
route=$/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=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 1 rule is reproducible but no production observation is claimed
batch59-task-extraction-m1-r5
multilingual receipt extraction
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$multilingual receipt extraction; candidate model; schema validation rate; key accuracy %; type conformance %; schema refusal rate; extraction reliability verdict; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 1 rule is reproducible but no production observation is claimed
batch59-task-extraction-m1-r6
unsupported schema property
route=$/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=2026-09-07Unavailable — 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 IDExact identity and evidence fieldsResultState
batch59-task-extraction-m2-r1
1-page invoice (800 in / 150 out)
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$1-page invoice (800 in / 150 out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 2 rule is reproducible but no production observation is claimed
batch59-task-extraction-m2-r2
5-page contract (4K in / 500 out)
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$5-page contract (4K in / 500 out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 2 rule is reproducible but no production observation is claimed
batch59-task-extraction-m2-r3
medical record summary (8K in / 800 out)
route=$/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=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 2 rule is reproducible but no production observation is claimed
batch59-task-extraction-m2-r4
financial prospectus table (15K in / 1.5K out)
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$financial prospectus table (15K in / 1.5K out); document archetype; input tokens; output JSON tokens; cost per 1K documents; lowest-cost model; quality-qualified winner; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 2 rule is reproducible but no production observation is claimed
batch59-task-extraction-m2-r5
batch classification (200 in / 20 out)
route=$/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=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 2 rule is reproducible but no production observation is claimed
batch59-task-extraction-m2-r6
unresolved 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=2026-09-07Unavailable — 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 IDExact identity and evidence fieldsResultState
batch59-task-extraction-m3-r1
malformed JSON syntax error
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$malformed JSON syntax error; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07Unavailable — exact task-extraction evidence join is not closed for "malformed JSON syntax error"FAIL CLOSED — manual, probe, or source evidence required
batch59-task-extraction-m3-r2
missing mandatory property
route=$/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=2026-09-07Unavailable — exact task-extraction evidence join is not closed for "missing mandatory property"FAIL CLOSED — manual, probe, or source evidence required
batch59-task-extraction-m3-r3
hallucinated extra property
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$hallucinated extra property; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 3 rule is reproducible but no production observation is claimed
batch59-task-extraction-m3-r4
schema validation timeout
route=$/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=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 3 rule is reproducible but no production observation is claimed
batch59-task-extraction-m3-r5
provider rate limit 429
route=$/best-llm-for/data-extraction; owner=$task-extraction; scenario=$provider rate limit 429; error classification; retry strategy; secondary fallback model; cumulative cost impact; containment outcome; manual audit trigger; verified=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — module 3 rule is reproducible but no production observation is claimed
batch59-task-extraction-m3-r6
unrecoverable extraction defect
route=$/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=2026-09-07Unavailable — frozen task-extraction fixture requires an exact source, identity, and result receiptUNTESTED — 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.

#ModelProviderFitEvidenceTask price/MTokens/secContextScored on
1Muse Spark 1.3 ContributorMeta93100/1$0.121.0Mprice, context, evidence
2Gemini 2.5 Flash LiteLegacyGoogle81$0.161Mprice, context
3Amazon Nova MicroAmazon7599/1$0.06168128Kprice, context, speed, evidence
4Mistral Small 3.1Mistral7199/1$0.24121256Kprice, context, speed, evidence
5Amazon Nova LiteAmazon6985/1$0.10108300Kprice, context, speed, evidence
6GPT-OSS 20BGroq6980/1$0.121120131Kprice, context, speed, evidence
7CodestralMistral6899/1$0.42118256Kprice, context, speed, evidence
8GPT-OSS 120B (Cerebras)Cerebras6872/1$0.432450131Kprice, context, speed, evidence

What will Structured Data Extraction cost?

At 100,000 structured extraction batch calls/month:

ModelTask price/MEst. 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?

Meta provider hubMuse Spark 1.3 Contributor pricingBest LLM for CodingBest LLM for Math & ReasoningBest LLM for Chatbots & Support

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.

Run this exact prompt against the top 3

Don't take a ranking's word for it — try Muse Spark 1.3 Contributor and its closest alternatives on your own prompt.

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