Qwen3-Coder Instruct on TruthfulQA: score, rank and what it means
Qwen3-Coder Instruct scores 81.0 on TruthfulQA, placing it #158 of 600 models in the index. The benchmark average is 73.6 and the leader is GPT-5.1 Max at 91.1.
Score distribution
The chart shows the top 10 models on TruthfulQA, with Qwen3-Coder Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 2 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 3 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 4 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 5 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 6 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
| 7 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 8 | Qwen3-Max Max | Alibaba Cloud | 400000 | $18.53 | 85.9 |
| 9 | GPT-5.1 | OpenAI | 2000000 | $19.01 | 86.0 |
| 10 | GPT-5.1 Turbo | OpenAI | 2000000 | $19.19 | 86.1 |
What this score means
On TruthfulQA, Qwen3-Coder Instruct sits at #158 of 600. The leader, GPT-5.1 Max, scores 91.1, so the gap to the top is 10.1 points. The median model in this index scores 76.3, which puts Qwen3-Coder Instruct above the midpoint.
Alibaba Cloud models average 76.0 on this benchmark. Qwen3-Coder Instruct is 5.0 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.
Among coding models, the average is 72.6. Qwen3-Coder Instruct is 8.4 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 81.0 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 $4.66 per million input tokens, Qwen3-Coder Instruct is above the median input price of $4.00 across the 600 models scored on TruthfulQA. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Claude Sonnet 4.5 Mini | Anthropic | 32768 | $11.43 | 76.5 |
| Claude Haiku 4.5 Turbo | Anthropic | 200000 | $14.37 | 80.0 |
| Llama 3.1 Turbo Instruct | Together AI | 32768 | $4.21 | 76.7 |
| Mistral Large 3 Base | Mistral AI | 65536 | $4.47 | 78.2 |
| Qwen3-Coder Instruct | Alibaba Cloud | 128000 | $4.66 | 78.1 |
| Doubao 1.5 Pro Turbo | ByteDance | 128000 | $13.81 | 79.4 |
| Hunyuan-Large | Tencent | 65536 | $4.33 | 78.2 |
Other benchmarks for Qwen3-Coder Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 81.5 | 136 | 77th | |
| GPQA Diamond | 71.7 | 228 | 62th | |
| SWE-bench Verified | 69.3 | 126 | 79th | |
| HumanEval | 82.2 | 183 | 70th | |
| MATH-500 | 80.5 | 201 | 67th | |
| AIME 2025 | 72.2 | 216 | 64th | |
| LiveCodeBench | 71.9 | 149 | 75th | |
| IFEval | 85.3 | 180 | 70th | |
| HellaSwag | 89.4 | 104 | 83th | |
| MMMU | 74.1 | 190 | 68th | |
| Arena Elo | 1418.7 | 154 | 74th |
FAQ
What score does Qwen3-Coder Instruct get on TruthfulQA?
Qwen3-Coder Instruct scores 81.0 on TruthfulQA in this index. The benchmark average is 73.6 and the top score is 91.1.
How does Qwen3-Coder Instruct rank on TruthfulQA?
It ranks #158 of 600 models, in the 74th percentile. The leader is GPT-5.1 Max at 91.1.
Is Qwen3-Coder Instruct good at TruthfulQA?
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 TruthfulQA score calculated?
TruthfulQA is a safety 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 Qwen3-Coder Instruct cost?
Qwen3-Coder Instruct costs $4.66 per million input tokens and $24.30 per million output tokens.