Mistral Small 3.1 24B vs Qwen3.5-Flash
Qwen3.5-Flash costs 2.1x less per output token than Mistral Small 3.1 24B ($0.26 vs $0.555 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
Mistral Small 3.1 24B or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 28
- Mistral Small 3.1 24B only
- 10
- Qwen3.5-Flash only
- 27
- 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
Only Qwen3.5-Flash has a coding score, so no call is made.
One side not measured
Reasoning
GPQA Diamond
No call
Only Qwen3.5-Flash has a GPQA Diamond result, 82.8%.
Directional only
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.5-Flash has a MMLU Pro result, 84.1%.
Directional only
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
Mistral Small 3.1 24B $0.0006, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.5-Flash has the number, $0.40.
Directional only
At a glance
- Coding index
- Mistral Small 3.1 24B not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Math, 24.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Mistral Small 3.1 24B 1253 against Qwen3.5-Flash 1445, 32.2 points apart on a 0 to 100 scale.
- Output price
- Mistral Small 3.1 24B $0.555 against Qwen3.5-Flash $0.26 per million output tokens, 2.1x apart.
- Context window
- Mistral Small 3.1 24B takes 128K tokens, Qwen3.5-Flash 1M.
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-Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 28 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1244 | Only Qwen3.5-Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 83.3% | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| ALE-Bench | Not measured | 265.9 | Only Qwen3.5-Flash measured |
| LMArena Text: coding | 1309 | 1412 | Qwen3.5-Flash |
| LMArena Text: software and IT | 1301 | 1416 | Qwen3.5-Flash |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash 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 | Mistral Small 3.1 24B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Neither model has a result on 42 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.8% | Only Qwen3.5-Flash measured |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | 1278 | 1403 | Qwen3.5-Flash |
| LMArena Text: expert | 1257 | 1407 | Qwen3.5-Flash |
| LMArena Text: hard prompts in English | 1290 | 1409 | Qwen3.5-Flash |
| LMArena Vision: diagrams | 1119 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Vision: homework | 1158 | Not measured | Only Mistral Small 3.1 24B measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.5-Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 18.2% | Only Qwen3.5-Flash measured |
| LMArena Text: math | 1261 | 1407 | Qwen3.5-Flash |
| LMArena Text: mathematical work | 1271 | 1413 | Qwen3.5-Flash |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.1% | Only Qwen3.5-Flash measured |
| LegalBench (Vals AI) | Not measured | 84.3% | Only Qwen3.5-Flash measured |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | Not measured | 63.6% | Only Qwen3.5-Flash measured |
| TaxEval (Vals AI) | Not measured | 72.2% | Only Qwen3.5-Flash measured |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedCode (Vals AI) | Not measured | 33.0% | Only Qwen3.5-Flash measured |
| SimpleQA Verified | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | 1265 | 1415 | Qwen3.5-Flash |
| LMArena Text: medicine and healthcare | 1267 | 1407 | Qwen3.5-Flash |
| LMArena Text: legal and government | 1271 | 1398 | Qwen3.5-Flash |
| LMArena Text: business and finance | 1257 | 1394 | Qwen3.5-Flash |
| Neither model has a result on the other 8 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-Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | Mistral Small 3.1 24B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| DTBench | Not measured | 82.9% | Only Qwen3.5-Flash measured |
| LMCA | Not measured | 29.1% | Only Qwen3.5-Flash measured |
| LMArena Text | 1277 | 1397 | Qwen3.5-Flash |
| LMArena Text: instruction following | 1263 | 1374 | Qwen3.5-Flash |
| LMArena Text: creative writing | 1252 | 1343 | Qwen3.5-Flash |
| LMArena Vision | 1136 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Text: English | 1295 | 1404 | Qwen3.5-Flash |
| LMArena Text: non-English | 1255 | 1385 | Qwen3.5-Flash |
| LMArena Text: Chinese | 1253 | 1445 | Qwen3.5-Flash |
| LMArena Text: German | 1266 | 1390 | Qwen3.5-Flash |
| LMArena Text: Japanese | 1208 | 1369 | Qwen3.5-Flash |
| LMArena Text: Korean | 1207 | 1344 | Qwen3.5-Flash |
| LMArena Text: French | 1273 | 1413 | Qwen3.5-Flash |
| LMArena Text: Spanish | 1284 | 1400 | Qwen3.5-Flash |
| LMArena Text: Russian | 1263 | 1380 | Qwen3.5-Flash |
| LMArena Text: Polish | 1242 | 1376 | Qwen3.5-Flash |
| LMArena Text: multi-turn | 1270 | 1393 | Qwen3.5-Flash |
| LMArena Text: longer queries | 1299 | 1392 | Qwen3.5-Flash |
| LMArena Text: writing and language | 1268 | 1369 | Qwen3.5-Flash |
| LMArena Text: entertainment and media | 1250 | 1347 | Qwen3.5-Flash |
| LMArena Vision: English | 1157 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Vision: Chinese | 1155 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Vision: reading text | 1132 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Vision: humor | 1142 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Vision: captioning | 1153 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Vision: recognizing things | 1155 | Not measured | Only Mistral Small 3.1 24B measured |
| LMArena Vision: creative writing | 1123 | Not measured | Only Mistral Small 3.1 24B measured |
| Neither model has a result on the other 19 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-Flash | 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
Qwen3.5-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-Flash 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-Flash |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | No public weights listed |
| Released | March 17, 2025 | February 25, 2026 |
| Context window | 128K | 1M |
| Input $/Mtok | $0.351 | $0.065 |
| Output $/Mtok | $0.555 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Small 3.1 24B better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Mistral Small 3.1 24B or Qwen3.5-Flash?
Mistral Small 3.1 24B costs $0.351 per million input tokens and $0.555 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.
Which has the bigger context window, Mistral Small 3.1 24B or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 128K.
How many benchmarks is the Mistral Small 3.1 24B vs Qwen3.5-Flash comparison based on?
28 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-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.