DeepSeek V3.2 Exp vs MiniMax M2
MiniMax M2 leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 18.6 to 16.6, and DeepSeek V3.2 Exp costs 2.5x less per output token ($0.41 vs $1.02 per million). Updated October 22, 2026; 47 benchmarks have a result for both.
DeepSeek V3.2 Exp or MiniMax M2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 2.5x cheaper ($0.41 vs $1.02 per million) and work measured by LMArena WebDev or LiveCodeBench (Artificial Analysis run), where it leads.
- Shared results
- 47
- DeepSeek V3.2 Exp only
- 12
- MiniMax M2 only
- 3
- Like-for-like categories
- 8 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
MiniMax M2
MiniMax M2 leads, 18.6 against 16.6.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp leads, 1178 against 1143.
Same board, both measured
Long documents
Listed context window
MiniMax M2
MiniMax M2 takes 205K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, MiniMax M2 $0.0008 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
- DeepSeek V3.2 Exp not benchmarked, MiniMax M2 not benchmarked.
- Widest category
- Agentic work, 52.9 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against MiniMax M2 86.8%, 52.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against MiniMax M2 $1.02 per million output tokens, 2.5x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, MiniMax M2 205K.
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 | DeepSeek V3.2 Exp | Bars | MiniMax M2 | Ties |
|---|---|---|---|---|
| Coding | 3 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 5 | 1 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 0 | ||
| Web design | 6 | 1 | ||
| General | 16 | 1 | ||
| All shared boards | 39 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index33
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| SWE-bench Verified | Not measured | 61.0% | Only MiniMax M2 measured |
| LMArena WebDev | 1272 | 1297 | MiniMax M2 |
| LiveCodeBench (Artificial Analysis run) | 78.9% | 82.6% | MiniMax M2 |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 25.8% | DeepSeek V3.2 Exp |
| Terminal-Bench | Not measured | 30.0% | Only MiniMax M2 measured |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: coding | 1438 | 1371 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1438 | 1373 | DeepSeek V3.2 Exp |
| LMArena WebDev: HTML | 1281 | 1306 | MiniMax M2 |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 18.6 | MiniMax M2 |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | 86.8% | MiniMax M2 |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Vending-Bench 2 | Not measured | $161 | Only MiniMax M2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 77.7% | DeepSeek V3.2 Exp |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 13.7% | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts | 1429 | 1357 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1422 | 1338 | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts in English | 1444 | 1376 | DeepSeek V3.2 Exp |
| Kagi LLM Benchmark | 52.2% | 57.8% | MiniMax M2 |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | 78.3 | DeepSeek V3.2 Exp |
| AIME 2025 (Artificial Analysis run) | 87.7% | 78.3% | DeepSeek V3.2 Exp |
| LMArena Text: math | 1422 | 1351 | DeepSeek V3.2 Exp |
| LMArena Text: mathematical work | 1433 | 1368 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | 82.0% | DeepSeek V3.2 Exp |
| LMArena Text: science | 1443 | 1348 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1367 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1349 | DeepSeek V3.2 Exp |
| LMArena Text: business and finance | 1412 | 1336 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | 1143 | DeepSeek V3.2 Exp |
| Design Arena: UI components | 1164 | 1137 | DeepSeek V3.2 Exp |
| Design Arena: game dev | 1144 | 1118 | DeepSeek V3.2 Exp |
| Design Arena: data viz | 1152 | 1140 | DeepSeek V3.2 Exp |
| Design Arena: 3D | 1148 | 1098 | DeepSeek V3.2 Exp |
| Design Arena: all code categories | 1170 | 1135 | DeepSeek V3.2 Exp |
| Design Arena: SVG | 1031 | 1100 | MiniMax M2 |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index161
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | Leader |
|---|---|---|---|
| IFBench | 54.1% | 72.3% | MiniMax M2 |
| Long context reasoning (AA-LCR) | 72.3% | 64.3% | DeepSeek V3.2 Exp |
| DTBench | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1340 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1401 | 1329 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1404 | 1286 | DeepSeek V3.2 Exp |
| LMArena Text: English | 1441 | 1364 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1313 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1461 | 1366 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1440 | 1357 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: French | 1434 | 1336 | DeepSeek V3.2 Exp |
| LMArena Text: Spanish | 1441 | 1326 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1412 | 1330 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | 1435 | 1312 | DeepSeek V3.2 Exp |
| LMArena Text: multi-turn | 1422 | 1361 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1418 | 1331 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1407 | 1295 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1407 | 1298 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | MiniMax M2 | 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
DeepSeek V3.2 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp 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 | DeepSeek V3.2 Exp | MiniMax M2 |
|---|---|---|
| Maker | DeepSeek | MiniMax |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | October 23, 2025 |
| Context window | 164K | 205K |
| Input $/Mtok | $0.27 | $0.255 |
| Output $/Mtok | $0.41 | $1.02 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.702 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than MiniMax M2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 2.5x cheaper ($0.41 vs $1.02 per million) and work measured by LMArena WebDev or LiveCodeBench (Artificial Analysis run), where it leads.
Which is cheaper, DeepSeek V3.2 Exp or MiniMax M2?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; MiniMax M2 costs $0.255 and $1.02. Cost per solved task is known for MiniMax M2 only.
Which has the bigger context window, DeepSeek V3.2 Exp or MiniMax M2?
MiniMax M2, with 205K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs MiniMax M2 comparison based on?
47 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and MiniMax M2's on 1. Where only one model has a result, the page says so instead of naming a winner.