GPT-5.3-Codex vs Mistral Small 3.2 24B
Updated October 21, 2026. 5 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Mistral Small 3.2 24B has none, so the two cannot be ranked on coding.
Mistral Small 3.2 24B has the edge where output tokens cost 46.7x less.
- Shared results
- 5
- GPT-5.3-Codex only
- 29
- Mistral Small 3.2 24B only
- 0
- 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
Only GPT-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.3-Codex has a AA Intelligence Index result, 32.5.
Directional only
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1163 against 896.
Same board, both measured
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.2 24B
GPT-5.3-Codex $0.0088, 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-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, Mistral Small 3.2 24B not benchmarked.
- Widest category
- Web design, 31.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: game dev: GPT-5.3-Codex 1161 against Mistral Small 3.2 24B 888, 34.1 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Mistral Small 3.2 24B $0.30 per million output tokens, 46.7x apart.
- Context window
- GPT-5.3-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-5.3-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 Index12 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | Not measured | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: React | 1395 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | 1422 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | Not measured | Only GPT-5.3-Codex measured |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1053 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 86.0% | Not measured | Only GPT-5.3-Codex measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | Not measured | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 19 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 896 | GPT-5.3-Codex |
| Design Arena: UI components | 1137 | 907 | GPT-5.3-Codex |
| Design Arena: game dev | 1161 | 888 | GPT-5.3-Codex |
| Design Arena: data viz | 1163 | 929 | GPT-5.3-Codex |
| Design Arena: 3D | 1008 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1145 | 907 | GPT-5.3-Codex |
| Design Arena: SVG | 1129 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | 1151 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | Not measured | Only GPT-5.3-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-5.3-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-5.3-Codex | Mistral Small 3.2 24B |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | February 24, 2026 | June 20, 2025 |
| Context window | 400K | 256K |
| Input $/Mtok | $1.75 | $0.10 |
| Output $/Mtok | $14.00 | $0.30 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Mistral Small 3.2 24B for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Mistral Small 3.2 24B has none, so the two cannot be ranked on coding. Mistral Small 3.2 24B has the edge where output tokens cost 46.7x less.
Which is cheaper, GPT-5.3-Codex or Mistral Small 3.2 24B?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Mistral Small 3.2 24B costs $0.10 and $0.30. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Mistral Small 3.2 24B?
GPT-5.3-Codex, with 400K tokens against 256K.
How many benchmarks is the GPT-5.3-Codex vs Mistral Small 3.2 24B comparison based on?
5 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 3 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.