MiniMax-Text 7B on SWE-bench Verified: score, rank and what it means
MiniMax-Text 7B scores 42.7 on SWE-bench Verified, placing it #494 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.
Score distribution
The chart shows the top 10 models on SWE-bench Verified, with MiniMax-Text 7B highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 2 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 3 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 4 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
| 5 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 6 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 7 | o4 Max | OpenAI | 2000000 | $19.51 | 86.2 |
| 8 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
What this score means
On SWE-bench Verified, MiniMax-Text 7B sits at #494 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 39.0 points. The median model in this index scores 61.0, which puts MiniMax-Text 7B below the midpoint.
MiniMax models average 57.0 on this benchmark. MiniMax-Text 7B is 14.3 points below that provider line, so it is a weaker result than the average MiniMax model.
Among small open-weight models, the average is 39.0. MiniMax-Text 7B is 3.7 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 42.7 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $1.10 per million input tokens, MiniMax-Text 7B is below the median input price of $4.00 across the 600 models scored on SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Mistral Small 3 7B | Mistral AI | 4096 | $0.89 | 55.2 |
| Llama 3 DeepInfra 8B | DeepInfra | 8192 | $1.05 | 57.4 |
| MiniMax-M2 1.5B | MiniMax | 16384 | $0.84 | 54.6 |
| Command R+ 8B | Cohere | 8192 | $1.21 | 57.7 |
| MiniMax-Text 7B | MiniMax | 16384 | $1.10 | 57.7 |
| Hunyuan-Large 8B | Tencent | 4096 | $1.29 | 58.7 |
| Llama 3 Novita 7B | Novita AI | 8192 | $0.92 | 56.3 |
Other benchmarks for MiniMax-Text 7B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 63.0 | 489 | 19th | |
| GPQA Diamond | 54.6 | 471 | 22th | |
| HumanEval | 60.2 | 503 | 16th | |
| MATH-500 | 59.9 | 467 | 22th | |
| AIME 2025 | 48.5 | 466 | 22th | |
| LiveCodeBench | 47.6 | 470 | 22th | |
| IFEval | 68.0 | 489 | 19th | |
| TruthfulQA | 63.0 | 493 | 18th | |
| HellaSwag | 73.6 | 463 | 23th | |
| MMMU | 53.4 | 515 | 14th | |
| Arena Elo | 1253.5 | 484 | 19th |
FAQ
What score does MiniMax-Text 7B get on SWE-bench Verified?
MiniMax-Text 7B scores 42.7 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does MiniMax-Text 7B rank on SWE-bench Verified?
It ranks #494 of 600 models, in the 18th percentile. The leader is GPT-5.2 Pro at 81.7.
Is MiniMax-Text 7B good at SWE-bench Verified?
It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.
How is the SWE-bench Verified score calculated?
SWE-bench Verified is a coding benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does MiniMax-Text 7B cost?
MiniMax-Text 7B costs $1.10 per million input tokens and $4.16 per million output tokens.