GPT-5.1-Codex-Max vs Mistral Small 4
Updated October 21, 2026. 2 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GPT-5.1-Codex-Max has the edge where the context window is larger, 400K against 262K tokens.
- Shared results
- 2
- GPT-5.1-Codex-Max only
- 4
- Mistral Small 4 only
- 12
- Like-for-like categories
- 1 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 Small 4 has a AA Intelligence Index result, 11.3.
Directional only
Agentic work
AA Agentic Index
No call
Only Mistral Small 4 has a AA Agentic Index result, 0.8.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 4
GPT-5.1-Codex-Max $0.0063, Mistral Small 4 $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Mistral Small 4 not benchmarked.
- Widest category
- Coding, 23.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-5.1-Codex-Max 1228.3 against Mistral Small 4 497.6, 24.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Max $10.00 against Mistral Small 4 $0.60 per million output tokens, 16.7x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Mistral Small 4 262K.
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-5.1-Codex-Max | Bars | Mistral Small 4 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| All shared boards | 2 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 26.6 | Only Mistral Small 4 measured |
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | 22.2% | 0.0% | GPT-5.1-Codex-Max |
| SciCode | Not measured | 38.8% | Only Mistral Small 4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 17.4% | Only Mistral Small 4 measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | 497.6 | GPT-5.1-Codex-Max |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 11.3 | Only Mistral Small 4 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.8 | Only Mistral Small 4 measured |
| τ²-Bench Telecom | Not measured | 41.2% | Only Mistral Small 4 measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.9% | Only Mistral Small 4 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 9.9% | Only Mistral Small 4 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | Leader |
|---|---|---|---|
| IFBench | Not measured | 48.2% | Only Mistral Small 4 measured |
| Long context reasoning (AA-LCR) | Not measured | 49.7% | Only Mistral Small 4 measured |
| DTBench | Not measured | 70.9% | Only Mistral Small 4 measured |
| LMCA | Not measured | 20.6% | Only Mistral Small 4 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Small 4 | 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 Small 4 costs less
Repository review
50K input + 3K output tokens
Mistral Small 4 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 4 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-5.1-Codex-Max | Mistral Small 4 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | December 4, 2025 | March 16, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.15 |
| Output $/Mtok | $10.00 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Mistral Small 4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-5.1-Codex-Max has the edge where the context window is larger, 400K against 262K tokens.
Which is cheaper, GPT-5.1-Codex-Max or Mistral Small 4?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Mistral Small 4 costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Mistral Small 4?
GPT-5.1-Codex-Max, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Max vs Mistral Small 4 comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Max's coding index rests on 1 independent benchmark and Mistral Small 4's on 1. Where only one model has a result, the page says so instead of naming a winner.