Codestral 2508 vs Qwen2.5 VL 72B Instruct
Codestral 2508 and Qwen2.5 VL 72B Instruct cost about the same per output token ($0.90 vs $1.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
Codestral 2508 or Qwen2.5 VL 72B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Codestral 2508 for output tokens 1.1x cheaper ($0.90 vs $1.00 per million) and a larger context window (256K against 128K tokens).
- Shared results
- 1
- Codestral 2508 only
- 7
- Qwen2.5 VL 72B Instruct only
- 5
- 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
Neither has a coding score yet.
One side not measured
Web design
Design Arena: websites
No call
Only Codestral 2508 has a Design Arena: websites result, 1013.
Directional only
Long documents
Listed context window
Codestral 2508
Codestral 2508 takes 256K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, Qwen2.5 VL 72B Instruct not benchmarked.
- Widest category
- Reasoning, 3.5 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: Codestral 2508 32.5% against Qwen2.5 VL 72B Instruct 36.0%, 3.5 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 1.1x apart.
- Context window
- Codestral 2508 takes 256K tokens, Qwen2.5 VL 72B Instruct 128K.
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 | Qwen2.5 VL 72B Instruct | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| ALE-Bench | 137.8 | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Kagi LLM Benchmark | 32.5% | 36.0% | Qwen2.5 VL 72B Instruct |
| 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 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GeoBench | Not measured | 62.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | Not measured | Only Codestral 2508 measured |
| Design Arena: UI components | 1017 | Not measured | Only Codestral 2508 measured |
| Design Arena: game dev | 968 | Not measured | Only Codestral 2508 measured |
| Design Arena: data viz | 1017 | Not measured | Only Codestral 2508 measured |
| Design Arena: 3D | 1020 | Not measured | Only Codestral 2508 measured |
| Design Arena: all code categories | 1007 | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| LMArena Vision | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: English | Not measured | 1115 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | Qwen2.5 VL 72B Instruct | 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 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | August 1, 2025 | February 1, 2025 |
| Context window | 256K | 128K |
| Input $/Mtok | $0.30 | $0.80 |
| Output $/Mtok | $0.90 | $1.00 |
| 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 Qwen2.5 VL 72B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Codestral 2508 for output tokens 1.1x cheaper ($0.90 vs $1.00 per million) and a larger context window (256K against 128K tokens).
Which is cheaper, Codestral 2508 or Qwen2.5 VL 72B Instruct?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or Qwen2.5 VL 72B Instruct?
Codestral 2508, with 256K tokens against 128K.
How many benchmarks is the Codestral 2508 vs Qwen2.5 VL 72B Instruct comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and Qwen2.5 VL 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.