Codestral 2508 vs DeepSeek V3.1
Codestral 2508 costs 1.7x less per output token than DeepSeek V3.1 ($0.90 vs $1.50 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
Codestral 2508 or DeepSeek V3.1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Codestral 2508 for output tokens 1.7x cheaper ($0.90 vs $1.50 per million) and a larger context window (256K against 164K tokens).
- Shared results
- 7
- Codestral 2508 only
- 1
- DeepSeek V3.1 only
- 4
- 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 DeepSeek V3.1 has a AA Intelligence Index result, 13.7.
Directional only
Web design
Design Arena: websites
DeepSeek V3.1
DeepSeek V3.1 leads, 1123 against 1013.
Same board, both measured
Long documents
Listed context window
Codestral 2508
Codestral 2508 takes 256K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, DeepSeek V3.1 $0.0013 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, DeepSeek V3.1 not benchmarked.
- Widest category
- Reasoning, 20.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: Codestral 2508 32.5% against DeepSeek V3.1 53.2%, 20.7 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against DeepSeek V3.1 $1.50 per million output tokens, 1.7x apart.
- Context window
- Codestral 2508 takes 256K tokens, DeepSeek V3.1 164K.
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 | DeepSeek V3.1 | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| Web design | 0 | 6 | ||
| All shared boards | 0 | 7 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | Codestral 2508 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| ALE-Bench | 137.8 | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 13.7 | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Codestral 2508 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| Kagi LLM Benchmark | 32.5% | 53.2% | DeepSeek V3.1 |
| Neither model has a result on the other 21 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | DeepSeek V3.1 | 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 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Codestral 2508 | DeepSeek V3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1123 | DeepSeek V3.1 |
| Design Arena: UI components | 1017 | 1085 | DeepSeek V3.1 |
| Design Arena: game dev | 968 | 1084 | DeepSeek V3.1 |
| Design Arena: data viz | 1017 | 1095 | DeepSeek V3.1 |
| Design Arena: 3D | 1020 | 1077 | DeepSeek V3.1 |
| Design Arena: all code categories | 1007 | 1112 | DeepSeek V3.1 |
| Design Arena: SVG | Not measured | 967 | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | DeepSeek V3.1 | 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 | DeepSeek V3.1 |
|---|---|---|
| Maker | Mistral AI | DeepSeek |
| Weights | No public weights listed | Open weights |
| Released | August 1, 2025 | August 21, 2025 |
| Context window | 256K | 164K |
| Input $/Mtok | $0.30 | $0.55 |
| Output $/Mtok | $0.90 | $1.50 |
| 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 Codestral 2508 better than DeepSeek V3.1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Codestral 2508 for output tokens 1.7x cheaper ($0.90 vs $1.50 per million) and a larger context window (256K against 164K tokens).
Which is cheaper, Codestral 2508 or DeepSeek V3.1?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; DeepSeek V3.1 costs $0.55 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or DeepSeek V3.1?
Codestral 2508, with 256K tokens against 164K.
How many benchmarks is the Codestral 2508 vs DeepSeek V3.1 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 DeepSeek V3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.