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Measurement sources and verification status (12 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: 115

latency: 0.38

contextWindow: 262144

inputPrice: 0.3

outputPrice: 1

benchmarkMmlu: 81.2

benchmarkHumanEval: 92.7

benchmarkMath: 81

benchmarkGpqa: 50

reasoningScore: 65

codingScore: 92.7

benchmarkSweBench: 51

Qwen: Qwen3 Coder 480B A35B

qwen🔒 Proprietary

Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...

LLM Stats Scores

65.0

Reasoning

92.7

Coding

🎯

50.0%GPQA Diamond — graduate-level science accuracy

Published benchmark evidence

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

51.0%

SWE-bench

Quality Score

76.0

Overall (0–100)

Speed

115 c/s

characters/second

Latency (TTFT)

0.38s

to first token

Input Price

$0.3

per 1M tokens

Output Price

per 1M tokens

Context Window

262K

tokens

Release Date

2025-07-23

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond50.0%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.

🧠

MMLU81.2%Solid

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

💻

HumanEval92.7%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 Benchmark81.0%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%.