Best LLM for Long Documents & RAG in 2026
For long documents & rag, Muse Spark 1.3 Contributor is our pick: $0.10/M tokens on a Long-document Q&A workload, 1.0M context.
Feeding a whole document or codebase into a single call needs headroom well beyond the text itself — retrieval overhead, system prompts, and chat history all eat into the window. We require at least 200K tokens of context and rank primarily on window size.
Quick answer: What is the best LLM for long documents and RAG?
Muse Spark 1.3 Contributor, from Meta, is the best fit for long documents & rag at $0.10 per million task tokens on a Long-document Q&A workload, with a 1.0M-token context window. No cheaper value pick beats it for this task.
What evidence supports the Long Documents & RAG recommendation?
Reproducible Long Documents & RAG 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 Long Documents & RAG →Volume, requirement-gate, and pricing cross-check for Long Documents & RAG
Monthly spend ladder at Long-document Q&A shape
| Calls / month | Overall pick monthly | Budget pick monthly | Overall − budget delta |
|---|---|---|---|
| 1,000 | $15.20 | $15.20 | $0.0000 |
| 2,000 | $30.40 | $30.40 | $0.0000 |
| 4,000 | $60.80 | $60.80 | $0.0000 |
| 10,000 | $152.00 | $152.00 | $0.0000 |
Monthly cost = task price/M × (150,000 input + 1,000 output tokens) × calls ÷ 1,000,000, at 0.5×, 1×, 2×, 5× the published 2,000-call/month baseline.
Requirement-gate margin for the picked models
| Model | Requirement | Measured value | Margin / result |
|---|---|---|---|
| Muse Spark 1.3 Contributor | 200K required | 1.0M measured | +849K headroom |
| Muse Spark 1.3 Contributor | 200K required | 1.0M measured | +849K headroom |
| Gemini 3.1 Pro | 200K required | 2M measured | +1.8M headroom |
Requirement gates are hard filters, not down-ranking: a model failing any row here is excluded from Long Documents & RAG 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 | $0.10 | $30.40 | Unavailable |
| GLM 4.7 (Cerebras) | $2.25 | $680.50 | 1980 tok/s |
| Gemini 3.1 Pro | $2.07 | $624.00 | 55 tok/s |
Evidence coverage: 0 of 40 candidates have a graded run. No graded accuracy evidence exists for this task; the ranking above is a requirements-and-price match, not a quality claim.
Test the Long Documents & RAG 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 long-document architecture envelope
1. Usable-window occupancy
| Model | Occupancy % | Raw window fraction | Fixed overhead (2K sys + 4K ret + 2K out) | 1.4x headroom floor | Usable document budget | Winner fit |
|---|---|---|---|---|---|---|
| Muse Spark 1.3 Contributor | 25% | 262,144 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 181,531 | Fits workload |
| Muse Spark 1.3 Contributor | 50% | 524,288 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 368,777 | Fits workload |
| Muse Spark 1.3 Contributor | 75% | 786,432 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 556,022 | Fits workload |
| Muse Spark 1.3 Contributor | 90% | 943,718.4 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 668,370 | Fits workload |
| Gemini 2.5 Flash Lite | 25% | 250,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 172,857 | Fits workload |
| Gemini 2.5 Flash Lite | 50% | 500,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 351,428 | Fits workload |
| Gemini 2.5 Flash Lite | 75% | 750,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 530,000 | Fits workload |
| Gemini 2.5 Flash Lite | 90% | 900,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 637,142 | Fits workload |
| Gemini 2.5 Flash | 25% | 250,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 172,857 | Fits workload |
| Gemini 2.5 Flash | 50% | 500,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 351,428 | Fits workload |
| Gemini 2.5 Flash | 75% | 750,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 530,000 | Fits workload |
| Gemini 2.5 Flash | 90% | 900,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 637,142 | Fits workload |
| Gemini 3.1 Pro | 25% | 500,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 351,428 | Fits workload |
| Gemini 3.1 Pro | 50% | 1,000,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 708,571 | Fits workload |
| Gemini 3.1 Pro | 75% | 1,500,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 1,065,714 | Fits workload |
| Gemini 3.1 Pro | 90% | 1,800,000 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 1,280,000 | Fits workload |
| Gemini 3.7 Flash | 25% | 262,144 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 181,531 | Fits workload |
| Gemini 3.7 Flash | 50% | 524,288 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 368,777 | Fits workload |
| Gemini 3.7 Flash | 75% | 786,432 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 556,022 | Fits workload |
| Gemini 3.7 Flash | 90% | 943,718.4 | 2,000 + 4,000 + 2,000 = 8,000 | 1.4× | 668,370 | Fits workload |
Usable document budget = floor((raw window fraction − 2,000 system − 4,000 retrieval − 2,000 output) ÷ 1.4). Context capacity is not recall quality.
2. Chunk-overlap inflation
| Chunk size | Overlap % | Net effective tokens/chunk | Chunks for 100K doc | Tokens resent (overlap) | Monthly bill (1K docs) |
|---|---|---|---|---|---|
| 8K | 0% | 8,000 | 13 | 0 tokens | $12.08 |
| 8K | 10% | 7,200 | 14 | 10,400 tokens | $13.28 |
| 8K | 20% | 6,400 | 16 | 24,000 tokens | $14.96 |
| 32K | 0% | 32,000 | 4 | 0 tokens | $10.64 |
| 32K | 10% | 28,800 | 4 | 9,600 tokens | $11.60 |
| 32K | 20% | 25,600 | 4 | 19,200 tokens | $12.56 |
| 64K | 0% | 64,000 | 2 | 0 tokens | $10.32 |
| 64K | 10% | 57,600 | 2 | 6,400 tokens | $10.96 |
| 64K | 20% | 51,200 | 2 | 12,800 tokens | $11.60 |
Net effective tokens/chunk = chunk × (1 − overlap). Chunks for 100K doc = ceil(100,000 ÷ net effective tokens). Tokens resent = (chunks − 1) × overlap tokens. Monthly bill is for 1,000 documents using exact MODEL_PRICING input rates on (100,000 + tokens resent) input tokens plus output rates on (chunks × 800) output tokens.
3. Single-pass vs hierarchical map-reduce
| Calls | Topology | Reference cost | Critical path | Quality |
|---|---|---|---|---|
| 1 | single pass | $0.02 | Unavailable | Recall/fidelity: Unavailable |
| 2 | 1 map (concurrent) + 1 reduce | $0.02 | Unavailable | Recall/fidelity: Unavailable |
| 4 | 3 map (concurrent) + 1 reduce | $0.02 | Unavailable | Recall/fidelity: Unavailable |
Critical-path latency assumes concurrent map worker execution plus serial final reduce call. Map-reduce reference cost accounts for both map inputs/outputs and reduce input (concatenated map outputs) + final output. 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.
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: Analyzing long documents (100K to 2M tokens) requires high retrieval fidelity across the entire context window, resistance to middle-context degradation, and cost-effective input pricing. Gemini and Claude lead this capability. Verified 2026-09-07.
Demand evidence: Qualitative demand: long-context document analysis benchmarks reviewed 2026-09-07; exact US monthly volume is unavailable.
Scope boundary: Compare top AI models for long-context document analysis, needle-in-a-haystack retrieval, legal/financial PDF comprehension, and long-input pricing. Exact joins required; unresolved joins render Unavailable.
Needle-in-a-haystack retrieval & context fidelity frontier
Deterministic formula / rule: retrieval_rate = correct_needle_answers / total_needle_tests across 0%, 25%, 50%, 75%, 100% depth.
Boundary: Owns long-context retrieval benchmark evaluation.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch59-task-longdocs-m1-r1needle at 50K tokens (start) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$needle at 50K tokens (start); model ID; context capacity; 0-25% depth score; 25-75% middle-depth score; 75-100% end-depth score; lost-in-the-middle vulnerability grade; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m1-r2needle at 128K tokens (middle) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$needle at 128K tokens (middle); model ID; context capacity; 0-25% depth score; 25-75% middle-depth score; 75-100% end-depth score; lost-in-the-middle vulnerability grade; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m1-r3needle at 500K tokens (middle-deep) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$needle at 500K tokens (middle-deep); model ID; context capacity; 0-25% depth score; 25-75% middle-depth score; 75-100% end-depth score; lost-in-the-middle vulnerability grade; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m1-r4needle at 1M tokens (end) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$needle at 1M tokens (end); model ID; context capacity; 0-25% depth score; 25-75% middle-depth score; 75-100% end-depth score; lost-in-the-middle vulnerability grade; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m1-r5multi-needle cross-reference test | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$multi-needle cross-reference test; model ID; context capacity; 0-25% depth score; 25-75% middle-depth score; 75-100% end-depth score; lost-in-the-middle vulnerability grade; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 1 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m1-r6unsupported context length | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$unsupported context length; model ID; context capacity; 0-25% depth score; 25-75% middle-depth score; 75-100% end-depth score; lost-in-the-middle vulnerability grade; verified=2026-09-07 | Unavailable — exact task-longdocs evidence join is not closed for "unsupported context length" | FAIL CLOSED — manual, probe, or source evidence required |
First-party citation: All AI Ask model specifications. Verified 2026-09-07; missing or conflicting joins fail closed.
Large document batch analysis unit economics
Deterministic formula / rule: doc_cost = (page_count * tokens_per_page * in_rate) + (synthesis_tokens * out_rate); cache/batch discount.
Boundary: Owns large document processing cost calculations.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch59-task-longdocs-m2-r150-page legal brief (25K tokens) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$50-page legal brief (25K tokens); document archetype; total input tokens; standard processing cost; cached/batch processing cost; lowest-cost model; recommendation tier; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m2-r2200-page SEC 10-K report (100K tokens) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$200-page SEC 10-K report (100K tokens); document archetype; total input tokens; standard processing cost; cached/batch processing cost; lowest-cost model; recommendation tier; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m2-r3500-page technical manual (250K tokens) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$500-page technical manual (250K tokens); document archetype; total input tokens; standard processing cost; cached/batch processing cost; lowest-cost model; recommendation tier; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m2-r4multi-file code repository (750K tokens) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$multi-file code repository (750K tokens); document archetype; total input tokens; standard processing cost; cached/batch processing cost; lowest-cost model; recommendation tier; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m2-r5multi-book library archive (1.5M tokens) | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$multi-book library archive (1.5M tokens); document archetype; total input tokens; standard processing cost; cached/batch processing cost; lowest-cost model; recommendation tier; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 2 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m2-r6unsupported document volume | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$unsupported document volume; document archetype; total input tokens; standard processing cost; cached/batch processing cost; lowest-cost model; recommendation tier; verified=2026-09-07 | Unavailable — exact task-longdocs evidence join is not closed for "unsupported document volume" | 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.
Whole-document vs RAG chunking decision framework
Deterministic formula / rule: architecture = (doc_tokens <= context_threshold && query_frequency == high) ? full_context_cache : vector_rag.
Boundary: Owns architectural decision between full-context LLM and vector RAG.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch59-task-longdocs-m3-r1frequently queried single 100K doc | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$frequently queried single 100K doc; corpus size; monthly query volume; full-context monthly cost; vector RAG monthly cost; accuracy trade-off; architectural recommendation; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m3-r2infrequently queried 500K doc collection | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$infrequently queried 500K doc collection; corpus size; monthly query volume; full-context monthly cost; vector RAG monthly cost; accuracy trade-off; architectural recommendation; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m3-r3massive 100M token multi-tenant library | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$massive 100M token multi-tenant library; corpus size; monthly query volume; full-context monthly cost; vector RAG monthly cost; accuracy trade-off; architectural recommendation; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m3-r4complex cross-document synthesis query | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$complex cross-document synthesis query; corpus size; monthly query volume; full-context monthly cost; vector RAG monthly cost; accuracy trade-off; architectural recommendation; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m3-r5high-precision verbatim quote extraction | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$high-precision verbatim quote extraction; corpus size; monthly query volume; full-context monthly cost; vector RAG monthly cost; accuracy trade-off; architectural recommendation; verified=2026-09-07 | Unavailable — frozen task-longdocs fixture requires an exact source, identity, and result receipt | UNTESTED — module 3 rule is reproducible but no production observation is claimed |
batch59-task-longdocs-m3-r6unsupported corpus architecture | route=$/best-llm-for/long-documents; owner=$task-longdocs; scenario=$unsupported corpus architecture; corpus size; monthly query volume; full-context monthly cost; vector RAG monthly cost; accuracy trade-off; architectural recommendation; verified=2026-09-07 | Unavailable — exact task-longdocs evidence join is not closed for "unsupported corpus architecture" | FAIL CLOSED — manual, probe, or source evidence required |
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-longdocs evidence flow →
Which models rank highest for Long Documents & RAG?
"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 | 78 | — | $0.10 | — | 1.0M | price, context |
| 2 | Gemini 2.5 Flash LiteLegacy | 76 | — | $0.10 | — | 1M | price, context | |
| 3 | Gemini 2.5 FlashLegacy | 70 | — | $0.31 | — | 1M | price, context | |
| 4 | Gemini 3.1 Pro | 69 | — | $2.07 | 55 | 2M | price, context, speed | |
| 5 | Gemini 3.7 Flash | 67 | — | $0.77 | — | 1.0M | price, context | |
| 6 | Muse Spark 1.3 | Meta | 64 | — | $1.27 | — | 1.0M | price, context |
| 7 | GLM-5.2 | Z.ai | 62 | — | $1.42 | — | 1M | price, context |
| 8 | Gemini 3.5 Flash Lite | 60 | — | $0.31 | 162 | 1M | price, context, speed |
What will Long Documents & RAG cost?
At 2,000 long-document q&a calls/month:
| Model | Task price/M | Est. monthly cost |
|---|---|---|
| Muse Spark 1.3 Contributor | $0.10 | $30.40 |
| Gemini 2.5 Flash Lite | $0.10 | $30.80 |
| Gemini 2.5 Flash | $0.31 | $95.00 |
How is the best LLM for Long Documents & RAG ranked?
Weights: evidence 0%, price 25%, speed 15%, context 60%.
Requirements: ≥200K context. 40 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.
What related resources help with Long Documents & RAG?
Where can you find evidence and costs for Long Documents & RAG?
What are common questions about the best LLM for Long Documents & RAG?
How much context headroom do I actually need?
Budget for your document plus retrieval overhead, system prompt, and conversation history — a 150K-token document comfortably needs a 200K+ window, not exactly 150K.
Does a bigger context window mean better recall inside it?
Not necessarily — window size is a hard capacity limit, not a quality guarantee. Very long prompts can still see recall degrade in the middle of the context ("lost in the middle").
Is RAG still worth it if the context window is huge?
Often yes — retrieval keeps cost and latency down by sending only relevant passages instead of the whole corpus, even when the model could technically fit everything.