Codestral 2508 vs Qwen3.5-27B
Codestral 2508 costs 1.7x less per output token than Qwen3.5-27B ($0.90 vs $1.56 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
Codestral 2508 or Qwen3.5-27B: 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 $1.56 per million) and work measured by ALE-Bench, where it leads.
- Shared results
- 1
- Codestral 2508 only
- 6
- Qwen3.5-27B only
- 52
- 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
Intelligence
AA Intelligence Index
No call
Only Qwen3.5-27B has a AA Intelligence Index result, 22.9.
Directional only
Web design
Design Arena: websites
No call
Only Codestral 2508 has a Design Arena: websites result, 1013.
Directional only
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Coding, 7.1 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Codestral 2508 137.8 against Qwen3.5-27B 349.4, 7.1 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against Qwen3.5-27B $1.56 per million output tokens, 1.7x apart.
- Context window
- Codestral 2508 takes 256K tokens, Qwen3.5-27B 262K.
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 | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1358 | Only Qwen3.5-27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only Qwen3.5-27B measured |
| WeirdML | Not measured | 39.5% | Only Qwen3.5-27B measured |
| ALE-Bench | 137.8 | 349.4 | Qwen3.5-27B |
| LMArena Text: coding | Not measured | 1427 | Only Qwen3.5-27B measured |
| LMArena Text: software and IT | Not measured | 1431 | Only Qwen3.5-27B measured |
| LMArena WebDev: React | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | Not measured | 1386 | Only Qwen3.5-27B 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 | Codestral 2508 | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 22.9 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | Qwen3.5-27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 93.9% | Only Qwen3.5-27B measured |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Codestral 2508 | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.8% | Only Qwen3.5-27B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.9% | Only Qwen3.5-27B measured |
| LMArena Text: hard prompts | Not measured | 1415 | Only Qwen3.5-27B measured |
| LMArena Text: expert | Not measured | 1427 | Only Qwen3.5-27B measured |
| LMArena Text: hard prompts in English | Not measured | 1429 | Only Qwen3.5-27B measured |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1429 | Only Qwen3.5-27B measured |
| LMArena Text: mathematical work | Not measured | 1429 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Codestral 2508 | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1437 | Only Qwen3.5-27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1425 | Only Qwen3.5-27B measured |
| LMArena Text: legal and government | Not measured | 1405 | Only Qwen3.5-27B measured |
| LMArena Text: business and finance | Not measured | 1399 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | Qwen3.5-27B | 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 index29 boards, none shared
| Benchmark | Codestral 2508 | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.6% | Only Qwen3.5-27B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.7% | Only Qwen3.5-27B measured |
| DTBench | Not measured | 82.4% | Only Qwen3.5-27B measured |
| LMCA | Not measured | 34.0% | Only Qwen3.5-27B measured |
| LMArena Text | Not measured | 1409 | Only Qwen3.5-27B measured |
| LMArena Text: instruction following | Not measured | 1393 | Only Qwen3.5-27B measured |
| LMArena Text: creative writing | Not measured | 1362 | Only Qwen3.5-27B measured |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | Not measured | 1425 | Only Qwen3.5-27B measured |
| LMArena Text: non-English | Not measured | 1390 | Only Qwen3.5-27B measured |
| LMArena Text: Chinese | Not measured | 1478 | Only Qwen3.5-27B measured |
| LMArena Text: German | Not measured | 1392 | Only Qwen3.5-27B measured |
| LMArena Text: Japanese | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena Text: Korean | Not measured | 1359 | Only Qwen3.5-27B measured |
| LMArena Text: French | Not measured | 1410 | Only Qwen3.5-27B measured |
| LMArena Text: Spanish | Not measured | 1407 | Only Qwen3.5-27B measured |
| LMArena Text: Russian | Not measured | 1390 | Only Qwen3.5-27B measured |
| LMArena Text: Polish | Not measured | 1375 | Only Qwen3.5-27B measured |
| LMArena Text: multi-turn | Not measured | 1410 | Only Qwen3.5-27B measured |
| LMArena Text: longer queries | Not measured | 1413 | Only Qwen3.5-27B measured |
| LMArena Text: writing and language | Not measured | 1380 | Only Qwen3.5-27B measured |
| LMArena Text: entertainment and media | Not measured | 1363 | Only Qwen3.5-27B measured |
| LMArena Vision: English | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | Qwen3.5-27B | 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
Qwen3.5-27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-27B 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 | Qwen3.5-27B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | August 1, 2025 | February 25, 2026 |
| Context window | 256K | 262K |
| Input $/Mtok | $0.30 | $0.195 |
| Output $/Mtok | $0.90 | $1.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than Qwen3.5-27B 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 $1.56 per million) and work measured by ALE-Bench, where it leads.
Which is cheaper, Codestral 2508 or Qwen3.5-27B?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 256K.
How many benchmarks is the Codestral 2508 vs Qwen3.5-27B 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 Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.