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?

DRAFT — placeholder editorial, not approved. Models under 200,000 tokens of context are left out on purpose. The score weighs the context window, the input rate this long prompt actually pays, and a cited MRCR result. A long window is not a promise that the model uses the whole document well.

How this pick is chosen

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

Muse Spark 1.3

$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

DeepSeek V4 Flash

$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

Qwen3.8 Max

$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

DRAFT — placeholder. Do not treat this as a reason to avoid the pick. A finished note should warn that a long context window does not mean the model will use the whole document well.

DRAFT — placeholder. Prompt caching matters more here than on short chats. The calculator has a cache-hit field for that.

Change the workload

Prices are USD per 1M tokens. A month is 30 days. Cache hits use the cache-read rate when the model has one; otherwise the cache field is ignored.

monthly = requests/day × 30 × (input tokens × (1 − cache hit) × input price + input tokens × cache hit × cache-read price + output tokens × output price) ÷ 1,000,000

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

Top 12 of 160 models at the reference workload, highest job score first. Scores are shown to one decimal. 148 more cleared the bar and rank lower.
ScoreModelMonthlyInputOutputContextProviders
111.9Muse Spark 1.3$94.64$1.25$4.251,048,57616
107.6DeepSeek V4 Pro$98.71$1.32$3.961,000,00058
106.1Qwen3.8 Max$151$2.00$6.001,000,00032
102.9Muse Spark 1.2$94.64$1.25$4.251,048,57618
94.8Qwen3.7 Plus$30.35$0.40$1.601,000,00030
94.8Qwen3.5 Plus$33.70$0.40$2.401,000,00011
94.7DeepSeek V4 Flash$10.60$0.14$0.281,000,00052
93.8Nemotron 3 Ultra 550B A55B$39.02$0.50$2.501,000,00022
93.8Qwen3.6 Plus$38.48$0.50$3.001,000,00021
92.6GPT-6 Luna$7.64$0.10$0.501,050,00032
92.5Mistral Large 4$51.27$0.68$2.091,048,5769
91.4GPT-5.6 Luna$15.39$0.20$1.201,050,00044