Mistral Large 2407 vs Summarize
Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $6.00
- Coding
- No data
- Overall
- No data
- Output $/M
- No data
Mistral Large 2407 or Summarize: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Mistral Large 2407 has results on LiveCodeBench (Artificial Analysis run), AA Intelligence Index and τ²-Bench Telecom; Summarize has none yet. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mistral Large 2407 has a AA Intelligence Index result, 7.6.
Directional only
Long documents
Listed context window
Summarize
Summarize takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Summarize
Mistral Large 2407 $0.0050, Summarize $0.0000 per turn.
List prices
At a glance
- Coding index
- Mistral Large 2407 not benchmarked, Summarize not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Mistral Large 2407 $6.00 against Summarize Free per million output tokens.
- Context window
- Mistral Large 2407 takes 131K tokens, Summarize 1M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board measured
| Benchmark | Mistral Large 2407 | Summarize | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 26.7% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Mistral Large 2407 | Summarize | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Mistral Large 2407 | Summarize | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Mistral Large 2407 | Summarize | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 47.2% | Not measured | Only Mistral Large 2407 measured |
| Humanity's Last Exam (Artificial Analysis run) | 2.9% | Not measured | Only Mistral Large 2407 measured |
| GPQA Diamond (Epoch AI run) | 49.0% | Not measured | Only Mistral Large 2407 measured |
| SimpleBench | 22.5% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Mistral Large 2407 | Summarize | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | Not measured | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | 0.0% | Not measured | Only Mistral Large 2407 measured |
| OTIS Mock AIME | 8.5% | Not measured | Only Mistral Large 2407 measured |
| MATH level 5 | 44.8% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Mistral Large 2407 | Summarize | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 68.3% | Not measured | Only Mistral Large 2407 measured |
| MMLU | 80.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 16 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Mistral Large 2407 | Summarize | Leader |
|---|---|---|---|
| IFBench | 31.6% | Not measured | Only Mistral Large 2407 measured |
| Long context reasoning (AA-LCR) | 2.0% | Not measured | Only Mistral Large 2407 measured |
| DTBench | 61.2% | Not measured | Only Mistral Large 2407 measured |
| Creative writing (Lech Mazur) | 69.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 42 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Summarize costs less
Repository review
50K input + 3K output tokens
Summarize costs less
Agent loop
200K input + 10K output tokens
Summarize costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Mistral Large 2407 | ScaleDown Summarize |
|---|---|---|
| Maker | Mistral AI | Scaledown |
| Weights | No public weights listed | No public weights listed |
| Released | November 19, 2024 | October 13, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $2.00 | $0.04 |
| Output $/Mtok | $6.00 | Free |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large 2407 better than Summarize for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Mistral Large 2407 or Summarize?
Mistral Large 2407 costs $2.00 per million input tokens and $6.00 per million output tokens; Summarize costs $0.04 and Free. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 2407 or Summarize?
Summarize, with 1M tokens against 131K.
Has any benchmark tested both Mistral Large 2407 and Summarize?
No benchmark has tested both yet. Mistral Large 2407 has results on LiveCodeBench (Artificial Analysis run), AA Intelligence Index and τ²-Bench Telecom; Summarize has none yet. Compare them on price and context instead. Data as of October 21, 2026.