Mistral Small 3.1 24B vs Qwen3.5-27B
Mistral Small 3.1 24B costs 2.8x less per output token than Qwen3.5-27B ($0.555 vs $1.56 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
Mistral Small 3.1 24B or Qwen3.5-27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Small 3.1 24B for output tokens 2.8x cheaper ($0.555 vs $1.56 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 38
- Mistral Small 3.1 24B only
- 0
- Qwen3.5-27B only
- 15
- Like-for-like categories
- 5 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
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.1 24B
Mistral Small 3.1 24B $0.0006, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- Mistral Small 3.1 24B not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Math, 27.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Mistral Small 3.1 24B 1253 against Qwen3.5-27B 1478, 37.6 points apart on a 0 to 100 scale.
- Output price
- Mistral Small 3.1 24B $0.555 against Qwen3.5-27B $1.56 per million output tokens, 2.8x apart.
- Context window
- Mistral Small 3.1 24B takes 128K 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 | Mistral Small 3.1 24B | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 25 | ||
| All shared boards | 0 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Mistral Small 3.1 24B | 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 | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | 1309 | 1427 | Qwen3.5-27B |
| LMArena Text: software and IT | 1301 | 1431 | Qwen3.5-27B |
| 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 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Small 3.1 24B | 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 | Mistral Small 3.1 24B | 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 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mistral Small 3.1 24B | 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 | 1278 | 1415 | Qwen3.5-27B |
| LMArena Text: expert | 1257 | 1427 | Qwen3.5-27B |
| LMArena Text: hard prompts in English | 1290 | 1429 | Qwen3.5-27B |
| LMArena Vision: diagrams | 1119 | 1249 | Qwen3.5-27B |
| LMArena Vision: homework | 1158 | 1256 | Qwen3.5-27B |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: math | 1261 | 1429 | Qwen3.5-27B |
| LMArena Text: mathematical work | 1271 | 1429 | Qwen3.5-27B |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: science | 1265 | 1437 | Qwen3.5-27B |
| LMArena Text: medicine and healthcare | 1267 | 1425 | Qwen3.5-27B |
| LMArena Text: legal and government | 1271 | 1405 | Qwen3.5-27B |
| LMArena Text: business and finance | 1257 | 1399 | Qwen3.5-27B |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index025
| Benchmark | Mistral Small 3.1 24B | 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 | 1277 | 1409 | Qwen3.5-27B |
| LMArena Text: instruction following | 1263 | 1393 | Qwen3.5-27B |
| LMArena Text: creative writing | 1252 | 1362 | Qwen3.5-27B |
| LMArena Vision | 1136 | 1241 | Qwen3.5-27B |
| LMArena Text: English | 1295 | 1425 | Qwen3.5-27B |
| LMArena Text: non-English | 1255 | 1390 | Qwen3.5-27B |
| LMArena Text: Chinese | 1253 | 1478 | Qwen3.5-27B |
| LMArena Text: German | 1266 | 1392 | Qwen3.5-27B |
| LMArena Text: Japanese | 1208 | 1346 | Qwen3.5-27B |
| LMArena Text: Korean | 1207 | 1359 | Qwen3.5-27B |
| LMArena Text: French | 1273 | 1410 | Qwen3.5-27B |
| LMArena Text: Spanish | 1284 | 1407 | Qwen3.5-27B |
| LMArena Text: Russian | 1263 | 1390 | Qwen3.5-27B |
| LMArena Text: Polish | 1242 | 1375 | Qwen3.5-27B |
| LMArena Text: multi-turn | 1270 | 1410 | Qwen3.5-27B |
| LMArena Text: longer queries | 1299 | 1413 | Qwen3.5-27B |
| LMArena Text: writing and language | 1268 | 1380 | Qwen3.5-27B |
| LMArena Text: entertainment and media | 1250 | 1363 | Qwen3.5-27B |
| LMArena Vision: English | 1157 | 1246 | Qwen3.5-27B |
| LMArena Vision: Chinese | 1155 | 1301 | Qwen3.5-27B |
| LMArena Vision: reading text | 1132 | 1246 | Qwen3.5-27B |
| LMArena Vision: humor | 1142 | 1227 | Qwen3.5-27B |
| LMArena Vision: captioning | 1153 | 1170 | Qwen3.5-27B |
| LMArena Vision: recognizing things | 1155 | 1210 | Qwen3.5-27B |
| LMArena Vision: creative writing | 1123 | 1224 | Qwen3.5-27B |
| 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 | Mistral Small 3.1 24B | 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
Mistral Small 3.1 24B 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 Small 3.1 24B | Qwen3.5-27B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | Open weights |
| Released | March 17, 2025 | February 25, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.351 | $0.195 |
| Output $/Mtok | $0.555 | $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 Mistral Small 3.1 24B better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Small 3.1 24B for output tokens 2.8x cheaper ($0.555 vs $1.56 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Mistral Small 3.1 24B or Qwen3.5-27B?
Mistral Small 3.1 24B costs $0.351 per million input tokens and $0.555 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, Mistral Small 3.1 24B or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 128K.
How many benchmarks is the Mistral Small 3.1 24B vs Qwen3.5-27B comparison based on?
38 benchmarks have a result for both models as of October 22, 2026. Mistral Small 3.1 24B'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.