Coding · Groq

Mixtral Groq Instruct on SWE-bench Verified: score, rank and what it means

Mixtral Groq Instruct scores 57.8 on SWE-bench Verified, placing it #359 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
57.8
0–100 scale
Rank
#359
of 600
Percentile
40th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
23.9
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Mixtral Groq 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, Mixtral Groq Instruct sits at #359 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 23.9 points. The median model in this index scores 61.0, which puts Mixtral Groq Instruct below the midpoint.

Groq models average 51.3 on this benchmark. Mixtral Groq Instruct is 6.5 points above that provider line, so it is a stronger result than the average Groq model.

Among open-weight models, the average is 55.5. Mixtral Groq Instruct is 2.3 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 57.8 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 $3.25 per million input tokens, Mixtral Groq Instruct 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.

ModelProviderContextInput / 1MAvg score
Auto OpenRouter 32768 $7.53 70.2
Mixtral Turbo Base Together AI 32768 $2.53 69.7
Molmo Allen Institute for AI 32768 $2.49 68.5
o3 Nano OpenAI 32768 $7.78 70.6
Mixtral Groq Instruct Groq 16384 $3.25 72.5
Mixtral Turbo Together AI 65536 $3.20 72.4
Arctic Chat Snowflake 16384 $2.70 69.8

Other benchmarks for Mixtral Groq Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 73.9 356 41th
GPQA Diamond 65.7 346 42th
HumanEval 74.7 347 42th
MATH-500 77.0 280 53th
AIME 2025 70.6 245 59th
LiveCodeBench 66.2 270 55th
IFEval 80.2 329 45th
TruthfulQA 74.5 341 43th
HellaSwag 85.1 240 60th
MMMU 71.6 252 58th
Arena Elo 1372.7 308 49th

FAQ

What score does Mixtral Groq Instruct get on SWE-bench Verified?

Mixtral Groq Instruct scores 57.8 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Mixtral Groq Instruct rank on SWE-bench Verified?

It ranks #359 of 600 models, in the 40th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Mixtral Groq Instruct 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 Mixtral Groq Instruct cost?

Mixtral Groq Instruct costs $3.25 per million input tokens and $16.01 per million output tokens.

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