Model comparison
MiniMax-M2.5 vs Qwen2.5 7B Instruct
MiniMax-M2.5 is the stronger model overall, scoring 38.1 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 1.7× less per token, which makes it the better buy when MiniMax-M2.5's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
MiniMax
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. MiniMax-M2.5 scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where MiniMax-M2.5 leads 38.6 to 17.0.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.5.
- MiniMax-M2.5 accepts more context: 205K tokens versus 131K.
Side by side
| MiniMax-M2.5 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | MiniMax | Alibaba (Qwen) |
| Noometry Index | 38.1 | 28.6 |
| Released | 2026-02-12 | 2024-09 |
| Weights | Open | Open |
| Context window | 205K | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $0.30 | $0.17 |
| Output $ / M tokens | $1.20 | $0.70 |
| Results tracked | 33 | 15 |
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Category by category
Coding MiniMax-M2.5 leads
MiniMax-M2.5: 47.9 (#53), Qwen2.5 7B Instruct: 35.9 (#204)
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 75.8% | — |
| LMArena WebDev | 1386 | — |
| SWE-bench Multilingual | 68.3% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1380 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 618.17 | — |
Agentic & Tool Use MiniMax-M2.5 leads
MiniMax-M2.5: 30.2 (#70), Qwen2.5 7B Instruct: 23.5 (#117)
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 42.7% | — |
| BALROG | — | 7.8% |
| Vending-Bench 2 | -23.16 | — |
Reasoning MiniMax-M2.5 leads
MiniMax-M2.5: 17.6 (#281), Qwen2.5 7B Instruct: 14.3 (#317)
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| Epoch Capabilities Index | 146.68 | 118.44 |
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 55.2% | — |
| NYT Connections (extended) | 16.8% | — |
| ARC-AGI-1 | 63.7% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1372 | — |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
Math MiniMax-M2.5 leads
MiniMax-M2.5: 25.8 (#242), Qwen2.5 7B Instruct: 11.8 (#300)
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| ProofBench | 4% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1378 | — |
Knowledge MiniMax-M2.5 leads
MiniMax-M2.5: 38.6 (#132), Qwen2.5 7B Instruct: 17.0 (#281)
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| MMLU-Pro | — | 53.9% |
| Vectara Hallucination Rate | 9.1% | — |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1378 | — |
| MMLU | — | 72.9% |
Multilingual Not comparable
MiniMax-M2.5: 47.3 (#147), Qwen2.5 7B Instruct: —
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1338 | — |
| LMArena Chinese | 1392 | — |
| LMArena French | 1363 | — |
| LMArena German | 1361 | — |
| LMArena Japanese | 1169 | — |
| LMArena Korean | 1232 | — |
| LMArena Russian | 1359 | — |
| LMArena Spanish | 1354 | — |
Instruction Following MiniMax-M2.5 leads
MiniMax-M2.5: 71.7 (#143), Qwen2.5 7B Instruct: 63.4 (#226)
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1353 | — |
Long Context Not comparable
MiniMax-M2.5: 37.4 (#211), Qwen2.5 7B Instruct: —
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| CL-bench | 11.4% | — |
| CL-bench Life | 6.3% | — |
| LMArena Longer Query | 1367 | — |
Writing & Preference MiniMax-M2.5 leads
MiniMax-M2.5: 54.1 (#149), Qwen2.5 7B Instruct: 48.8 (#194)
| Benchmark | MiniMax-M2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1359 | — |
| LMArena Creative Writing | 1331 | — |
| EQ-Bench Creative Writing | 1361 | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1364 | — |
Frequently asked questions
Is MiniMax-M2.5 better than Qwen2.5 7B Instruct?
MiniMax-M2.5 is the stronger model overall, scoring 38.1 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 1.7× less per token, which makes it the better buy when MiniMax-M2.5's lead doesn't matter for your workload.
Which is cheaper, MiniMax-M2.5 or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; MiniMax-M2.5 lists at $0.30 and $1.20.
Is MiniMax-M2.5 or Qwen2.5 7B Instruct better for coding?
MiniMax-M2.5 scores higher on coding benchmarks: 47.9 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
MiniMax-M2.5 does, with 205K tokens against 131K.
How many benchmarks do MiniMax-M2.5 and Qwen2.5 7B Instruct share?
1 benchmark has published results for both models. MiniMax-M2.5 has 33 scored results on Noometry and Qwen2.5 7B Instruct has 15.