Pick for Long-document Q&A: Muse Spark 1.3 at $94.64 per month. Best mix of context window and the long-prompt price: 1,048,576-token window, billed at $1.25 per 1M input on this workload, MRCR 98.3. Prices from Models.dev. Data last refreshed Oct 14, 2026, 05:43 PM UTC.
Job
Long-document Q&A
Which model should I use to ask questions of documents over 200,000 tokens, and what will it cost each month?
Models under 200,000 tokens of context are left out. The score weighs the context window, the input rate this long prompt pays, and a cited MRCR result from the lab. A long window is not a promise that the model uses the whole document well.
How this pick is chosen
- Outputs text. Embeddings and image-only models are not in the catalog.
- Lab is one of: openai, anthropic, google, meta, mistral, deepseek, xai, alibaba, moonshotai, zhipuai, cohere, amazon, nvidia, microsoft, ai21.
- Listed by at least 5 providers. A model on only the lab and a reseller does not clear this.
- Not deprecated, and not an alpha release. Beta releases stay in.
- Not a specialized model: OCR, embeddings, rerankers, speech, safeguard, or moderation, matched on the name.
- Context window of at least 200,000 tokens.
- Has a numeric input and output price.
- Reference price is not $0. A zero in Models.dev is often an unpublished price.
- A benchmark counts only when its source is the model's lab (including that lab's Hugging Face org) or the benchmark owner. Artificial Analysis, OpenRouter, and LLM Stats are dropped. When one benchmark has several cited scores, the median is used.
- Long-document score: 100 times the log of the context window relative to 1,048,576 tokens, minus 10 times the log of one plus the input rate this workload actually pays (including a long-context tier). Compact names lose 22. Efficiency-tier descriptions lose 6. Preview and snapshot names lose 8. A name with no version digit loses 8. A cited MRCR score adds up to 24.
- The top pick is the highest job score. On coding agents, models within 4 points of that leader are treated as a flagship tie: the lower monthly bill is the top pick and the higher bill is the premium pick. Otherwise the premium pick is the best upgrade that costs more than the top pick. The budget pick is the highest job score that costs less than the top pick and sits at or below the 25th cost percentile. Output rates above $50 per 1M tokens stay out of the premium card. Summarization uses raw quality, not quality per dollar, when it chooses that upgrade. Remaining ties break to more providers, then a lower monthly cost, then a newer release date.
Fifteen long questions a day. Most of the bill is the document in the prompt, not the answer.
160 models cleared the bar.303 failed the lab, provider, or specialty bar.56 failed a capability filter.0 listed at $0 were left out.0 had no usable price.
Top pick
$94.64 / month
Best mix of context window and the long-prompt price: 1,048,576-token window, billed at $1.25 per 1M input on this workload, MRCR 98.3.
Score 111.9 · $1.25 input · $4.25 output per 1M · context 1,048,576 · 16 providers
Budget pick
$10.60 / month
Best long-context value among the cheaper models: 1,000,000-token window, billed at $0.14 per 1M input on this workload, MRCR 78.7.
Score 94.7 · $0.14 input · $0.28 output per 1M · context 1,000,000 · 52 providers
Premium pick
$151 / month
Best long-context mix among the models that cost more: 1,000,000-token window, billed at $2.00 per 1M input on this workload, MRCR 92.9.
Score 106.1 · $2.00 input · $6.00 output per 1M · context 1,000,000 · 32 providers
When not to use it
Skip this pick when the documents are short enough that a smaller window is cheaper, when you need retrieval over a corpus instead of one prompt, or when you want a lab-published long-context result and the card does not cite one. MRCR is one needle test, not a full reading of your files.
The reference workload is fifteen questions a day, about 180,000 input tokens and 1,200 output tokens, with a 10% cache hit. Most of the bill is the document. Prompt caching changes the number more here than on short chats.
The three cards follow the inputs. Fit, and who holds each role, stays on the reference workload.
Everyone who cleared the bar, best fit first