Codestral 2508 vs GPT-5.3-Codex
Codestral 2508 costs 15.6x less per output token than GPT-5.3-Codex ($0.90 vs $14.00 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
Codestral 2508 or GPT-5.3-Codex: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding.
Pick Codestral 2508 for output tokens 15.6x cheaper ($0.90 vs $14.00 per million) and work measured by Design Arena: 3D, where it leads.
- Shared results
- 7
- Codestral 2508 only
- 1
- GPT-5.3-Codex only
- 27
- 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
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 1013.
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
Codestral 2508
Codestral 2508 $0.0008, GPT-5.3-Codex $0.0088 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
- Codestral 2508 not benchmarked, GPT-5.3-Codex 60.0 from 3 benchmarks.
- Widest category
- Coding, 50.6 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Codestral 2508 137.8 against GPT-5.3-Codex 1655.2, 50.6 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-5.3-Codex $14.00 per million output tokens, 15.6x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-5.3-Codex 400K.
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 | Codestral 2508 | Bars | GPT-5.3-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Web design | 1 | 5 | ||
| All shared boards | 1 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1407 | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 87.3% | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 78.0% | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 61.8% | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | Not measured | 53.8% | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 53.0% | Only GPT-5.3-Codex measured |
| Terminal-Bench | Not measured | 78.4% | Only GPT-5.3-Codex measured |
| WeirdML | Not measured | 79.3% | Only GPT-5.3-Codex measured |
| ALE-Bench | 137.8 | 1655.2 | GPT-5.3-Codex |
| LMArena WebDev: React | Not measured | 1395 | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | Not measured | 1422 | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | Not measured | 1443 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 32.5 | Only GPT-5.3-Codex measured |
| Epoch Capabilities Index | Not measured | 157.1 | Only GPT-5.3-Codex measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1032 | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 983 | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1054 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1084 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1124 | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | Not measured | 86.0% | Only GPT-5.3-Codex measured |
| METR time horizons | Not measured | 74.5% | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | Not measured | $5,940 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 91.5% | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 42.5% | Only GPT-5.3-Codex measured |
| Kagi LLM Benchmark | 32.5% | Not measured | Only Codestral 2508 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 | Codestral 2508 | GPT-5.3-Codex | 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 | Codestral 2508 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Codestral 2508 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1163 | GPT-5.3-Codex |
| Design Arena: UI components | 1017 | 1137 | GPT-5.3-Codex |
| Design Arena: game dev | 968 | 1161 | GPT-5.3-Codex |
| Design Arena: data viz | 1017 | 1164 | GPT-5.3-Codex |
| Design Arena: 3D | 1020 | 1008 | Codestral 2508 |
| Design Arena: all code categories | 1007 | 1145 | GPT-5.3-Codex |
| Design Arena: SVG | Not measured | 1129 | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | Not measured | 1152 | 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 | Codestral 2508 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.4% | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 83.3% | 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 | Codestral 2508 | GPT-5.3-Codex | 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
Codestral 2508 costs less
Repository review
50K input + 3K output tokens
Codestral 2508 costs less
Agent loop
200K input + 10K output tokens
Codestral 2508 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 Codestral 2508 | OpenAI GPT-5.3-Codex |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | February 24, 2026 |
| Context window | 256K | 400K |
| Input $/Mtok | $0.30 | $1.75 |
| Output $/Mtok | $0.90 | $14.00 |
| Coding index | Not benchmarked | 60.0 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than GPT-5.3-Codex for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding. Pick Codestral 2508 for output tokens 15.6x cheaper ($0.90 vs $14.00 per million) and work measured by Design Arena: 3D, where it leads.
Which is cheaper, Codestral 2508 or GPT-5.3-Codex?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, Codestral 2508 or GPT-5.3-Codex?
GPT-5.3-Codex, with 400K tokens against 256K.
How many benchmarks is the Codestral 2508 vs GPT-5.3-Codex comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and GPT-5.3-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.