MiniMax M2 vs Voxtral Small 24B 2507
Voxtral Small 24B 2507 costs 3.4x less per output token than MiniMax M2 ($0.30 vs $1.02 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.02
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.30
MiniMax M2 or Voxtral Small 24B 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick MiniMax M2 for a larger context window (205K against 33K tokens).
No benchmark has tested both yet. MiniMax M2 has results on SWE-bench Verified, LMArena WebDev and LiveCodeBench (Artificial Analysis run); 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only MiniMax M2 has a AA Intelligence Index result, 18.6.
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
MiniMax M2
MiniMax M2 takes 205K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Voxtral Small 24B 2507
MiniMax M2 $0.0008, Voxtral Small 24B 2507 $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2 has the number, $0.702.
Directional only
At a glance
- Coding index
- MiniMax M2 not benchmarked, 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
- MiniMax M2 $1.02 against Voxtral Small 24B 2507 $0.30 per million output tokens, 3.4x apart.
- Context window
- MiniMax M2 takes 205K 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 Index8 boards measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| SWE-bench Verified | 61.0% | Not measured | Only MiniMax M2 measured |
| LMArena WebDev | 1297 | Not measured | Only MiniMax M2 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 |
| LMArena Text: coding | 1371 | Not measured | Only MiniMax M2 measured |
| LMArena Text: software and IT | 1373 | Not measured | Only MiniMax M2 measured |
| LMArena WebDev: HTML | 1306 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.6 | Not measured | Only MiniMax M2 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 measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | 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 | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| 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 |
| LMArena Text: hard prompts | 1357 | Not measured | Only MiniMax M2 measured |
| LMArena Text: expert | 1338 | Not measured | Only MiniMax M2 measured |
| LMArena Text: hard prompts in English | 1376 | Not measured | Only MiniMax M2 measured |
| Kagi LLM Benchmark | 57.8% | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| AA Math Index | 78.3 | Not measured | Only MiniMax M2 measured |
| AIME 2025 (Artificial Analysis run) | 78.3% | Not measured | Only MiniMax M2 measured |
| LMArena Text: math | 1351 | Not measured | Only MiniMax M2 measured |
| LMArena Text: mathematical work | 1368 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only MiniMax M2 measured |
| LMArena Text: science | 1348 | Not measured | Only MiniMax M2 measured |
| LMArena Text: medicine and healthcare | 1367 | Not measured | Only MiniMax M2 measured |
| LMArena Text: legal and government | 1349 | Not measured | Only MiniMax M2 measured |
| LMArena Text: business and finance | 1336 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index17 boards measured
| Benchmark | MiniMax M2 | Voxtral Small 24B 2507 | Leader |
|---|---|---|---|
| IFBench | 72.3% | Not measured | Only MiniMax M2 measured |
| Long context reasoning (AA-LCR) | 64.3% | Not measured | Only MiniMax M2 measured |
| LMArena Text | 1340 | Not measured | Only MiniMax M2 measured |
| LMArena Text: instruction following | 1329 | Not measured | Only MiniMax M2 measured |
| LMArena Text: creative writing | 1286 | Not measured | Only MiniMax M2 measured |
| LMArena Text: English | 1364 | Not measured | Only MiniMax M2 measured |
| LMArena Text: non-English | 1313 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Chinese | 1366 | Not measured | Only MiniMax M2 measured |
| LMArena Text: German | 1357 | Not measured | Only MiniMax M2 measured |
| LMArena Text: French | 1336 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Spanish | 1326 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Russian | 1330 | Not measured | Only MiniMax M2 measured |
| LMArena Text: Polish | 1312 | Not measured | Only MiniMax M2 measured |
| LMArena Text: multi-turn | 1361 | Not measured | Only MiniMax M2 measured |
| LMArena Text: longer queries | 1331 | Not measured | Only MiniMax M2 measured |
| LMArena Text: writing and language | 1295 | Not measured | Only MiniMax M2 measured |
| LMArena Text: entertainment and media | 1298 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 31 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 | MiniMax M2 | Mistral Voxtral Small 24B 2507 |
|---|---|---|
| Maker | MiniMax | Mistral AI |
| Weights | Open weights | Open weights |
| Released | October 23, 2025 | October 30, 2025 |
| Context window | 205K | 33K |
| Input $/Mtok | $0.255 | $0.10 |
| Output $/Mtok | $1.02 | $0.30 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $0.702 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2 better than Voxtral Small 24B 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick MiniMax M2 for a larger context window (205K against 33K tokens).
Which is cheaper, MiniMax M2 or Voxtral Small 24B 2507?
MiniMax M2 costs $0.255 per million input tokens and $1.02 per million output tokens; Voxtral Small 24B 2507 costs $0.10 and $0.30. Cost per solved task is known for MiniMax M2 only.
Which has the bigger context window, MiniMax M2 or Voxtral Small 24B 2507?
MiniMax M2, with 205K tokens against 33K.
Has any benchmark tested both MiniMax M2 and Voxtral Small 24B 2507?
No benchmark has tested both yet. MiniMax M2 has results on SWE-bench Verified, LMArena WebDev and LiveCodeBench (Artificial Analysis run); Voxtral Small 24B 2507 has none yet. Compare them on price and context instead. Data as of October 22, 2026.