MiniMax-Text Instruct on HellaSwag: score, rank and what it means
MiniMax-Text Instruct scores 85.9 on HellaSwag, placing it #220 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.
Score distribution
The chart shows the top 10 models on HellaSwag, with MiniMax-Text Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 3 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 4 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 8 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 9 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 10 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
What this score means
On HellaSwag, MiniMax-Text Instruct sits at #220 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 10.2 points. The median model in this index scores 83.6, which puts MiniMax-Text Instruct above the midpoint.
MiniMax models average 81.3 on this benchmark. MiniMax-Text Instruct is 4.6 points above that provider line, so it is a stronger result than the average MiniMax model.
Among multimodal models, the average is 86.8. MiniMax-Text Instruct is 0.9 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 85.9 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 $4.59 per million input tokens, MiniMax-Text Instruct is above the median input price of $4.00 across the 600 models scored on HellaSwag. 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 |
|---|---|---|---|---|
| Reka Flash Pro | Reka AI | 200000 | $10.82 | 75.2 |
| Sonar | Perplexity | 200000 | $10.57 | 76.0 |
| Llama 3 Anyscale | Anyscale | 65536 | $3.94 | 74.7 |
| Llama 3 Hyperbolic Instruct | Hyperbolic | 200000 | $3.79 | 76.2 |
| MiniMax-Text Instruct | MiniMax | 128000 | $4.59 | 77.7 |
| Yi-Lightning Instruct | 01.AI | 128000 | $4.16 | 76.5 |
| Gemini 3 Pro Mini | Google DeepMind | 128000 | $12.27 | 78.1 |
Other benchmarks for MiniMax-Text Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 78.7 | 222 | 63th | |
| GPQA Diamond | 72.8 | 202 | 66th | |
| SWE-bench Verified | 69.3 | 125 | 79th | |
| HumanEval | 81.5 | 198 | 67th | |
| MATH-500 | 80.7 | 195 | 68th | |
| AIME 2025 | 78.5 | 107 | 82th | |
| LiveCodeBench | 68.1 | 225 | 63th | |
| IFEval | 87.6 | 112 | 81th | |
| TruthfulQA | 78.5 | 238 | 60th | |
| MMMU | 73.0 | 222 | 63th | |
| Arena Elo | 1429.1 | 123 | 80th |
FAQ
What score does MiniMax-Text Instruct get on HellaSwag?
MiniMax-Text Instruct scores 85.9 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.
How does MiniMax-Text Instruct rank on HellaSwag?
It ranks #220 of 600 models, in the 63rd percentile. The leader is GPT-5.2 Max at 96.1.
Is MiniMax-Text Instruct good at HellaSwag?
Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.
How is the HellaSwag score calculated?
HellaSwag is a commonsense 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 Instruct cost?
MiniMax-Text Instruct costs $4.59 per million input tokens and $21.17 per million output tokens.