Coding · Together AI

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

Mixtral Turbo Instruct scores 60.8 on SWE-bench Verified, placing it #309 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
60.8
0–100 scale
Rank
#309
of 600
Percentile
49th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
20.9
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Mixtral Turbo 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 Turbo Instruct sits at #309 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 20.9 points. The median model in this index scores 61.0, which puts Mixtral Turbo Instruct below the midpoint.

Together AI models average 52.1 on this benchmark. Mixtral Turbo Instruct is 8.7 points above that provider line, so it is a stronger result than the average Together AI model.

Among open-weight models, the average is 55.5. Mixtral Turbo Instruct is 5.3 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 60.8 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 $3.16 per million input tokens, Mixtral Turbo 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
Grok 4 Nano xAI 16384 $8.97 72.7
Nova Lite Amazon 65536 $9.20 73.4
OLMo 2 Chat Allen Institute for AI 16384 $2.94 70.6
Phi-3.5 Microsoft 16384 $2.81 70.9
Mixtral Turbo Instruct Together AI 32768 $3.16 71.9
Command R+ Base Cohere 65536 $3.24 71.6
Hunyuan-Large Base Tencent 200000 $3.64 74.0

Other benchmarks for Mixtral Turbo Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 72.9 369 39th
GPQA Diamond 69.7 276 54th
HumanEval 74.2 355 41th
MATH-500 75.9 303 50th
AIME 2025 67.5 304 49th
LiveCodeBench 61.4 358 40th
IFEval 82.1 275 54th
TruthfulQA 77.2 276 54th
HellaSwag 82.5 336 44th
MMMU 66.6 365 39th
Arena Elo 1356.6 351 42th

FAQ

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

Mixtral Turbo Instruct scores 60.8 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

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

It ranks #309 of 600 models, in the 49th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Mixtral Turbo 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 Turbo Instruct cost?

Mixtral Turbo Instruct costs $3.16 per million input tokens and $13.04 per million output tokens.

Related