GPT-5.1-Codex vs Mistral Small 3.2 24B
Mistral Small 3.2 24B costs 33.3x less per output token than GPT-5.1-Codex ($0.30 vs $10.00 per million). Updated October 21, 2026; 5 benchmarks have a result for both.
GPT-5.1-Codex or Mistral Small 3.2 24B: which to pick
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Mistral Small 3.2 24B has none, so the two cannot be ranked on coding.
Pick Mistral Small 3.2 24B for output tokens 33.3x cheaper ($0.30 vs $10.00 per million).
- Shared results
- 5
- GPT-5.1-Codex only
- 21
- Mistral Small 3.2 24B only
- 0
- Like-for-like categories
- 1 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
Only GPT-5.1-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.1-Codex has a AA Intelligence Index result, 23.7.
Directional only
Web design
Design Arena: websites
GPT-5.1-Codex
GPT-5.1-Codex leads, 1162 against 896.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.2 24B
GPT-5.1-Codex $0.0063, Mistral Small 3.2 24B $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex 15.3 from 2 benchmarks, Mistral Small 3.2 24B not benchmarked.
- Widest category
- Web design, 34.4 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: UI components: GPT-5.1-Codex 1237 against Mistral Small 3.2 24B 907, 41.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex $10.00 against Mistral Small 3.2 24B $0.30 per million output tokens, 33.3x apart.
- Context window
- GPT-5.1-Codex takes 400K tokens, Mistral Small 3.2 24B 256K.
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 | Bars | Mistral Small 3.2 24B | Ties |
|---|---|---|---|---|
| Web design | 5 | 0 | ||
| All shared boards | 5 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev | 1337 | Not measured | Only GPT-5.1-Codex measured |
| LiveCodeBench (Vals AI) | 85.5% | Not measured | Only GPT-5.1-Codex measured |
| Vibe Code Bench (Vals AI) | 13.1% | Not measured | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 84.9% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench | 57.8% | Not measured | Only GPT-5.1-Codex measured |
| ALE-Bench | 1244.9 | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev: HTML | 1346 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.7 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1018 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | 1086 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | 1191 | Not measured | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | 83.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 25.7% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| AA Math Index | 95.7 | Not measured | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | 95.7% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 17 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | 896 | GPT-5.1-Codex |
| Design Arena: UI components | 1237 | 907 | GPT-5.1-Codex |
| Design Arena: game dev | 1138 | 888 | GPT-5.1-Codex |
| Design Arena: data viz | 1213 | 929 | GPT-5.1-Codex |
| Design Arena: all code categories | 1152 | 907 | GPT-5.1-Codex |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| IFBench | 70.0% | Not measured | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | 69.3% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Small 3.2 24B | 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 3.2 24B costs less
Repository review
50K input + 3K output tokens
Mistral Small 3.2 24B costs less
Agent loop
200K input + 10K output tokens
Mistral Small 3.2 24B 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 | Mistral Small 3.2 24B |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | June 20, 2025 |
| Context window | 400K | 256K |
| Input $/Mtok | $1.25 | $0.10 |
| Output $/Mtok | $10.00 | $0.30 |
| Coding index | 15.3 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.892 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex better than Mistral Small 3.2 24B for coding?
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Mistral Small 3.2 24B has none, so the two cannot be ranked on coding. Pick Mistral Small 3.2 24B for output tokens 33.3x cheaper ($0.30 vs $10.00 per million).
Which is cheaper, GPT-5.1-Codex or Mistral Small 3.2 24B?
GPT-5.1-Codex costs $1.25 per million input tokens and $10.00 per million output tokens; Mistral Small 3.2 24B costs $0.10 and $0.30. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, GPT-5.1-Codex or Mistral Small 3.2 24B?
GPT-5.1-Codex, with 400K tokens against 256K.
How many benchmarks is the GPT-5.1-Codex vs Mistral Small 3.2 24B comparison based on?
5 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex's coding index rests on 2 independent benchmarks and Mistral Small 3.2 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.