GPT-5.6 Sol Pro vs Mistral Large 2407
Mistral Large 2407 costs 3.3x less per output token than GPT-5.6 Sol Pro ($6.00 vs $20.00 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
GPT-5.6 Sol Pro or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 131K tokens).
- Shared results
- 2
- GPT-5.6 Sol Pro only
- 5
- Mistral Large 2407 only
- 15
- Like-for-like categories
- 2 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
GPT-5.6 Sol Pro
GPT-5.6 Sol Pro takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 2407
GPT-5.6 Sol Pro $0.014, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-5.6 Sol Pro not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Reasoning, 49.2 points apart on average over 1 shared benchmark.
- Widest single gap
- SimpleBench: GPT-5.6 Sol Pro 71.7% against Mistral Large 2407 22.5%, 49.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol Pro $20.00 against Mistral Large 2407 $6.00 per million output tokens, 3.3x apart.
- Context window
- GPT-5.6 Sol Pro takes 1.05M tokens, Mistral Large 2407 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.6 Sol Pro | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 26.7% | Only Mistral Large 2407 measured |
| WeirdML | 89.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.6 | 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 index2 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 33.0% | Only Mistral Large 2407 measured |
| APEX-Agents | 51.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 47.2% | Only Mistral Large 2407 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 2.9% | Only Mistral Large 2407 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | 71.7% | 22.5% | GPT-5.6 Sol Pro |
| Chess puzzles | 68.0% | Not measured | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 0.0 | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 0.0% | Only Mistral Large 2407 measured |
| OTIS Mock AIME | Not measured | 8.5% | Only Mistral Large 2407 measured |
| FrontierMath, tier 4 | 80.5% | Not measured | Only GPT-5.6 Sol Pro measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Large 2407 measured |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | Not measured | 31.6% | Only Mistral Large 2407 measured |
| Long context reasoning (AA-LCR) | Not measured | 2.0% | Only Mistral Large 2407 measured |
| DTBench | 96.0% | 61.2% | GPT-5.6 Sol Pro |
| LMCA | 59.2% | Not measured | Only GPT-5.6 Sol Pro measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 5 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
Mistral Large 2407 costs less
Repository review
50K input + 3K output tokens
Mistral Large 2407 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 2407 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 | OpenAI GPT-5.6 Sol Pro | Mistral Large 2407 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | November 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $4.00 | $2.00 |
| Output $/Mtok | $20.00 | $6.00 |
| 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 GPT-5.6 Sol Pro better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 131K tokens).
Which is cheaper, GPT-5.6 Sol Pro or Mistral Large 2407?
GPT-5.6 Sol Pro costs $4.00 per million input tokens and $20.00 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.6 Sol Pro or Mistral Large 2407?
GPT-5.6 Sol Pro, with 1.05M tokens against 131K.
How many benchmarks is the GPT-5.6 Sol Pro vs Mistral Large 2407 comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. GPT-5.6 Sol Pro's coding index rests on 0 independent benchmarks and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.