Pick for RAG answering: Muse Spark 1.3 at $48.92 per month. Best cited long-context result for reading retrieved chunks: 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 16, 2026, 07:15 AM UTC.
Job
RAG answering
Which model should read retrieved chunks in a RAG pipeline, and what will it cost each month?
This ranks the model that reads retrieved text, not the embedding model. Embeddings and rerankers are not in this catalog. The score weighs the context window, the input rate on a chunk-sized prompt, and a cited MRCR result. Cards are chosen only from models with that citation. A long window is not proof the model uses every chunk.
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 128,000 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 A zero in Models.dev is often an unpublished price.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.
- RAG-reader score: 50 times the log of the context window relative to 1,048,576 tokens, minus 12 times the log of one plus the input rate this chunked workload pays, plus up to 36 from a cited MRCR score. A model with no cited MRCR loses 28. Compact names lose 10. This ranks the model that reads retrieved text. Embedding models are not in the catalog. The cards are chosen only from models with a cited MRCR score.
- 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.
120 questions a day. Each prompt is about 12,000 tokens of retrieved chunks plus the question, and the answer is about 600 tokens. 30% of the input is treated as cached.
205 models cleared the bar.304 failed the lab, provider, or specialty bar.12 failed a capability filter.1 listed at $0 were left out.0 had no usable price.
Top pick
$48.92 / month
Best cited long-context result for reading retrieved chunks: 1,048,576-token window, billed at $1.25 per 1M input on this workload, MRCR 98.3.
Score 71.3 · $1.25 input · $4.25 output per 1M · context 1,048,576 · 16 providers
Budget pick
$5.23 / month
Lower bill with a cited MRCR result: 1,000,000-token window, billed at $0.14 per 1M input on this workload, MRCR 78.7.
Score 65.9 · $0.14 input · $0.28 output per 1M · context 1,000,000 · 52 providers
Premium pick
$76.68 / month
Stronger cited MRCR result 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 64.3 · $2.00 input · $6.00 output per 1M · context 1,000,000 · 32 providers
When not to use it
Skip this page when you are choosing an embedding or a reranker, or when the whole document has to sit in one prompt. Whole-document questions are on the long-document page, and that workload is much larger.
The reference workload is 120 questions a day, about 12,000 tokens in and 600 out, with a 30% cache hit. Change the calculator for a denser retrieval setup. The cards reprice; they do not re-rank.
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