MiniMax M2 vs Qwen3.5-Flash
Qwen3.5-Flash costs 3.9x less per output token than MiniMax M2 ($0.26 vs $1.02 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
MiniMax M2 or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; MiniMax M2 has none, so the two cannot be ranked on coding.
Pick MiniMax M2 for work measured by LMArena WebDev or LMArena WebDev: HTML, where it leads.
- Shared results
- 28
- MiniMax M2 only
- 22
- 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
Intelligence
AA Intelligence Index
No call
Only MiniMax M2 has a AA Intelligence Index result, 18.6.
Directional only
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
Web design
Design Arena: websites
No call
Only MiniMax M2 has a Design Arena: websites result, 1143.
Directional only
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
MiniMax M2 $0.0008, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.5-Flash
MiniMax M2 $0.702, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- MiniMax M2 not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- General, 9.6 points apart on average over 15 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: MiniMax M2 1366 against Qwen3.5-Flash 1445, 13.2 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2 $1.02 against Qwen3.5-Flash $0.26 per million output tokens, 3.9x apart.
- Context window
- MiniMax M2 takes 205K 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 | MiniMax M2 | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 15 | ||
| All shared boards | 2 | 26 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | MiniMax M2 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 61.0% | Not measured | Only MiniMax M2 measured |
| LMArena WebDev | 1297 | 1244 | MiniMax M2 |
| 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 |
| LiveCodeBench (Artificial Analysis run) | 82.6% | Not measured | Only MiniMax M2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.8% | Not measured | Only MiniMax M2 measured |
| Terminal-Bench | 30.0% | Not measured | Only MiniMax M2 measured |
| ALE-Bench | Not measured | 265.9 | Only Qwen3.5-Flash measured |
| LMArena Text: coding | 1371 | 1412 | Qwen3.5-Flash |
| LMArena Text: software and IT | 1373 | 1416 | Qwen3.5-Flash |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | 1306 | 1291 | MiniMax M2 |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | MiniMax M2 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.6 | Not measured | Only MiniMax M2 measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | MiniMax M2 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 86.8% | Not measured | Only MiniMax M2 measured |
| Vending-Bench 2 | $161 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | MiniMax M2 | 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 (Artificial Analysis run) | 77.7% | Not measured | Only MiniMax M2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 13.7% | Not measured | Only MiniMax M2 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 | 1357 | 1403 | Qwen3.5-Flash |
| LMArena Text: expert | 1338 | 1407 | Qwen3.5-Flash |
| LMArena Text: hard prompts in English | 1376 | 1409 | Qwen3.5-Flash |
| Kagi LLM Benchmark | 57.8% | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | MiniMax M2 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| AA Math Index | 78.3 | Not measured | Only MiniMax M2 measured |
| AIME 2025 (Artificial Analysis run) | 78.3% | Not measured | Only MiniMax M2 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 | 1351 | 1407 | Qwen3.5-Flash |
| LMArena Text: mathematical work | 1368 | 1413 | Qwen3.5-Flash |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | MiniMax M2 | 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 |
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only MiniMax M2 measured |
| SimpleQA Verified | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | 1348 | 1415 | Qwen3.5-Flash |
| LMArena Text: medicine and healthcare | 1367 | 1407 | Qwen3.5-Flash |
| LMArena Text: legal and government | 1349 | 1398 | Qwen3.5-Flash |
| LMArena Text: business and finance | 1336 | 1394 | Qwen3.5-Flash |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | MiniMax M2 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1143 | Not measured | Only MiniMax M2 measured |
| Design Arena: UI components | 1134 | Not measured | Only MiniMax M2 measured |
| Design Arena: game dev | 1118 | Not measured | Only MiniMax M2 measured |
| Design Arena: data viz | 1140 | Not measured | Only MiniMax M2 measured |
| Design Arena: 3D | 1098 | Not measured | Only MiniMax M2 measured |
| Design Arena: all code categories | 1135 | Not measured | Only MiniMax M2 measured |
| Design Arena: SVG | 1100 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index015
| Benchmark | MiniMax M2 | 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 |
| IFBench | 72.3% | Not measured | Only MiniMax M2 measured |
| Long context reasoning (AA-LCR) | 64.3% | Not measured | Only MiniMax M2 measured |
| DTBench | Not measured | 82.9% | Only Qwen3.5-Flash measured |
| LMCA | Not measured | 29.1% | Only Qwen3.5-Flash measured |
| LMArena Text | 1340 | 1397 | Qwen3.5-Flash |
| LMArena Text: instruction following | 1329 | 1374 | Qwen3.5-Flash |
| LMArena Text: creative writing | 1286 | 1343 | Qwen3.5-Flash |
| LMArena Text: English | 1364 | 1404 | Qwen3.5-Flash |
| LMArena Text: non-English | 1313 | 1385 | Qwen3.5-Flash |
| LMArena Text: Chinese | 1366 | 1445 | Qwen3.5-Flash |
| LMArena Text: German | 1357 | 1390 | Qwen3.5-Flash |
| LMArena Text: Japanese | Not measured | 1369 | Only Qwen3.5-Flash measured |
| LMArena Text: Korean | Not measured | 1344 | Only Qwen3.5-Flash measured |
| LMArena Text: French | 1336 | 1413 | Qwen3.5-Flash |
| LMArena Text: Spanish | 1326 | 1400 | Qwen3.5-Flash |
| LMArena Text: Russian | 1330 | 1380 | Qwen3.5-Flash |
| LMArena Text: Polish | 1312 | 1376 | Qwen3.5-Flash |
| LMArena Text: multi-turn | 1361 | 1393 | Qwen3.5-Flash |
| LMArena Text: longer queries | 1331 | 1392 | Qwen3.5-Flash |
| LMArena Text: writing and language | 1295 | 1369 | Qwen3.5-Flash |
| LMArena Text: entertainment and media | 1298 | 1347 | Qwen3.5-Flash |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2 | 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 | MiniMax M2 | Qwen3.5-Flash |
|---|---|---|
| Maker | MiniMax | Qwen |
| Weights | Open weights | No public weights listed |
| Released | October 23, 2025 | February 25, 2026 |
| Context window | 205K | 1M |
| Input $/Mtok | $0.255 | $0.065 |
| Output $/Mtok | $1.02 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.702 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2 better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; MiniMax M2 has none, so the two cannot be ranked on coding. Pick MiniMax M2 for work measured by LMArena WebDev or LMArena WebDev: HTML, where it leads.
Which is cheaper, MiniMax M2 or Qwen3.5-Flash?
MiniMax M2 costs $0.255 per million input tokens and $1.02 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, MiniMax M2 costs $0.702 and Qwen3.5-Flash $0.40.
Which has the bigger context window, MiniMax M2 or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 205K.
How many benchmarks is the MiniMax M2 vs Qwen3.5-Flash comparison based on?
28 benchmarks have a result for both models as of October 22, 2026. MiniMax M2's coding index rests on 1 independent benchmark and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.