Coding · MiniMax

MiniMax-Text Instruct on SWE-bench Verified: score, rank and what it means

MiniMax-Text Instruct scores 69.3 on SWE-bench Verified, placing it #125 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
69.3
0–100 scale
Rank
#125
of 600
Percentile
79th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
12.4
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with MiniMax-Text Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg 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 Instruct sits at #125 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 12.4 points. The median model in this index scores 61.0, which puts MiniMax-Text Instruct above the midpoint.

MiniMax models average 57.0 on this benchmark. MiniMax-Text Instruct is 12.3 points above that provider line, so it is a stronger result than the average MiniMax model.

Among multimodal models, the average is 66.4. MiniMax-Text Instruct is 2.9 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 69.3 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 SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
GLM-4.5 Chat Zhipu AI 65536 $4.53 78.3
Qwen3-Coder Chat Alibaba Cloud 128000 $4.59 78.4
Llama 3.3 Meta AI 128000 $4.56 78.2
Grok 4 Mini xAI 65536 $12.21 77.9
MiniMax-Text Instruct MiniMax 128000 $4.59 77.7
Qwen3-Coder Instruct Alibaba Cloud 128000 $4.66 78.1
Claude Haiku 4.5 Anthropic 1000000 $13.86 79.4

Other benchmarks for MiniMax-Text Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.7 222 63th
GPQA Diamond 72.8 202 66th
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
HellaSwag 85.9 220 63th
MMMU 73.0 222 63th
Arena Elo 1429.1 123 80th

FAQ

What score does MiniMax-Text Instruct get on SWE-bench Verified?

MiniMax-Text Instruct scores 69.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does MiniMax-Text Instruct rank on SWE-bench Verified?

It ranks #125 of 600 models, in the 79th percentile. The leader is GPT-5.2 Pro at 81.7.

Is MiniMax-Text Instruct good at SWE-bench Verified?

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 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 Instruct cost?

MiniMax-Text Instruct costs $4.59 per million input tokens and $21.17 per million output tokens.

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