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Measurement sources and verification status (13 fields)

Collected third-party figures are not independent tests. A source link and collection date do not establish reproducibility. Exact model version, dataset and test settings may be missing. No independent verification date is supplied.

speed: 85

latency: 0.5

contextWindow: 262144

inputPrice: 0.14

outputPrice: 0.8

benchmarkMmlu: 91

benchmarkHumanEval: 92

benchmarkMath: 93

benchmarkHellaSwag: 91

benchmarkGpqa: 66

benchmarkMmluPro: 73

reasoningScore: 86

codingScore: 87

Qwen: Qwen3 235B A22B Instruct 2507

qwen🔒 Proprietary

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...

LLM Stats Scores

86.0

Reasoning

87.0

Coding

🎯

66.0%GPQA Diamond — graduate-level science accuracy

73.0%MMLU-Pro

Published benchmark evidence

Task scores are reported independently by each publisher; they are not combined into a hidden quality score.

View HLE source ↗

15.9%

Humanity's Last Exam

Quality Score

73.0

Overall (0–100)

Speed

85 c/s

characters/second

Latency (TTFT)

0.50s

to first token

Input Price

$0.14

per 1M tokens

Output Price

$0.8

per 1M tokens

Context Window

262K

tokens

Release Date

2025-07-21

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond66.0%Below avg

Designed so that even PhD-level human experts average ~65–70%. A score above 80% signals near-expert scientific reasoning that goes far beyond memorisation.

📚

MMLU-Pro73.0%Solid

The harder successor to MMLU — more options per question eliminate single-answer guessing. Tests depth of professional knowledge across 14 disciplines.

🧠

MMLU91.0%Top tier

The classic academic breadth benchmark. Top frontier models now score above 85%, so it best distinguishes mid-tier from strong generalist models.

💻

HumanEval92.0%Top tier

Measures whether a model writes functionally correct code — not just plausible-looking syntax. Strong scores (>85%) indicate reliable day-to-day coding assistance.

∑

MATH Benchmark93.0%Top tier

Tests multi-step mathematical reasoning under strict correctness. Even a 60% score requires strong chain-of-thought; top reasoning models now exceed 90%.

🌐

HellaSwag91.0%Solid

Tests grounded, everyday common sense rather than memorised facts. Most frontier models now score 90%+, so it best separates small/mid models from large ones.