Qwen3.8 27B vs Voxtral Small 24B 2507
Voxtral Small 24B 2507 costs 8.5x less per output token than Qwen3.8 27B ($0.30 vs $2.55 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- 74.2
- Overall
- 59.6
- Output $/M
- $2.55
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.30
Qwen3.8 27B or Voxtral Small 24B 2507: which to pick
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Voxtral Small 24B 2507 has none, so the two cannot be ranked on coding.
Pick Voxtral Small 24B 2507 for output tokens 8.5x cheaper ($0.30 vs $2.55 per million).
No benchmark has tested both yet. Qwen3.8 27B has results on LiveBench coding, LMArena WebDev and AA Coding Index; Voxtral Small 24B 2507 has none yet. Compare them on price and context instead.
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 Qwen3.8 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.8 27B has a AA Intelligence Index result, 33.7.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.8 27B has a GPQA Diamond result, 88.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 27B has a MMLU Pro result, 84.3%.
Directional only
Long documents
Listed context window
Qwen3.8 27B
Qwen3.8 27B takes 1M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Voxtral Small 24B 2507
Qwen3.8 27B $0.0017, Voxtral Small 24B 2507 $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- Qwen3.8 27B 74.2 from 4 benchmarks, Voxtral Small 24B 2507 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Qwen3.8 27B $2.55 against Voxtral Small 24B 2507 $0.30 per million output tokens, 8.5x apart.
- Context window
- Qwen3.8 27B takes 1M tokens, Voxtral Small 24B 2507 33K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index18 boards measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| LiveBench coding | 86.8% | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev | 1593 | Not measured | Only Qwen3.8 27B measured |
| AA Coding Index | 68.1 | Not measured | Only Qwen3.8 27B measured |
| LiveCodeBench (Vals AI) | 84.0% | Not measured | Only Qwen3.8 27B measured |
| SWE-bench Verified (Vals AI) | 86.0% | Not measured | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | 64.8% | Not measured | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | 14.2% | Not measured | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Qwen3.8 27B measured |
| IOI (Vals AI) | 39.1% | Not measured | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | 58.4% | Not measured | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | 38.1% | Not measured | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | 67.8% | Not measured | Only Qwen3.8 27B measured |
| SciCode | 46.6% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: coding | 1483 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: software and IT | 1478 | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev: React | 1602 | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | 1570 | Not measured | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | 1577 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | Not measured | Only Qwen3.8 27B measured |
| Epoch Capabilities Index | 151.7 | Not measured | Only Qwen3.8 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| AA Agentic Index | 45.8 | Not measured | Only Qwen3.8 27B measured |
| LiveBench research | 59.4% | Not measured | Only Qwen3.8 27B measured |
| LiveBench browser | 32.0% | Not measured | Only Qwen3.8 27B measured |
| LiveBench long agentic | 27.9% | Not measured | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | 48.5% | Not measured | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | 30.9% | Not measured | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | 36.1% | Not measured | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | 59.7% | Not measured | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only Qwen3.8 27B measured |
| APEX-Agents | 47.5% | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| GPQA Diamond | 88.9% | Not measured | Only Qwen3.8 27B measured |
| LiveBench reasoning | 80.0% | Not measured | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | 83.9% | Not measured | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | 90.5% | Not measured | Only Qwen3.8 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 33.9% | Not measured | Only Qwen3.8 27B measured |
| ARC-AGI | 87.5% | Not measured | Only Qwen3.8 27B measured |
| ARC-AGI-2 | 42.4% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts | 1459 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: expert | 1479 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts in English | 1461 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: diagrams | 1285 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: homework | 1295 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| ProofBench | 16.0% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: math | 1455 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: mathematical work | 1458 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| MMLU Pro | 84.3% | Not measured | Only Qwen3.8 27B measured |
| LegalBench (Vals AI) | 82.4% | Not measured | Only Qwen3.8 27B measured |
| TaxEval (Vals AI) | 70.8% | Not measured | Only Qwen3.8 27B measured |
| MortgageTax (Vals AI) | 64.9% | Not measured | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | 28.7% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: science | 1467 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: medicine and healthcare | 1437 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: legal and government | 1447 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: business and finance | 1438 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| LiveBench design | 75.9% | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index30 boards measured
| Benchmark | Qwen3.8 27B | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| LiveBench data analysis | 76.6% | Not measured | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | 52.4% | Not measured | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | 83.8% | Not measured | Only Qwen3.8 27B measured |
| Long context reasoning (AA-LCR) | 82.0% | Not measured | Only Qwen3.8 27B measured |
| DTBench | 88.0% | Not measured | Only Qwen3.8 27B measured |
| LMCA | 41.4% | Not measured | Only Qwen3.8 27B measured |
| Surface Evolver Bench | 45.0% | Not measured | Only Qwen3.8 27B measured |
| LMArena Text | 1440 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: instruction following | 1436 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: creative writing | 1383 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision | 1269 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: English | 1448 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: non-English | 1428 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Chinese | 1501 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: German | 1443 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Japanese | 1387 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Korean | 1384 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: French | 1465 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Spanish | 1449 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Russian | 1415 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: Polish | 1439 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | 1440 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: longer queries | 1448 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: writing and language | 1405 | Not measured | Only Qwen3.8 27B measured |
| LMArena Text: entertainment and media | 1393 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: English | 1267 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | 1326 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | 1281 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: humor | 1235 | Not measured | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | 1237 | Not measured | Only Qwen3.8 27B measured |
| Neither model has a result on the other 18 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
Voxtral Small 24B 2507 costs less
Repository review
50K input + 3K output tokens
Voxtral Small 24B 2507 costs less
Agent loop
200K input + 10K output tokens
Voxtral Small 24B 2507 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 | Qwen3.8 27B | Mistral Voxtral Small 24B 2507 |
|---|---|---|
| Maker | Qwen | Mistral AI |
| Weights | Open weights | Open weights |
| Released | August 14, 2026 | October 30, 2025 |
| Context window | 1M | 33K |
| Input $/Mtok | $0.425 | $0.10 |
| Output $/Mtok | $2.55 | $0.30 |
| Coding index | 74.2 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $1.26 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.8 27B better than Voxtral Small 24B 2507 for coding?
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Voxtral Small 24B 2507 has none, so the two cannot be ranked on coding. Pick Voxtral Small 24B 2507 for output tokens 8.5x cheaper ($0.30 vs $2.55 per million).
Which is cheaper, Qwen3.8 27B or Voxtral Small 24B 2507?
Qwen3.8 27B costs $0.425 per million input tokens and $2.55 per million output tokens; Voxtral Small 24B 2507 costs $0.10 and $0.30. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, Qwen3.8 27B or Voxtral Small 24B 2507?
Qwen3.8 27B, with 1M tokens against 33K.
Has any benchmark tested both Qwen3.8 27B and Voxtral Small 24B 2507?
No benchmark has tested both yet. Qwen3.8 27B has results on LiveBench coding, LMArena WebDev and AA Coding Index; Voxtral Small 24B 2507 has none yet. Compare them on price and context instead. Data as of October 22, 2026.