Kimi K1.5 Instruct on Arena Elo: score, rank and what it means
Kimi K1.5 Instruct scores 1431.8 on Arena Elo, placing it #118 of 600 models in the index. The benchmark average is 1349.9 and the leader is Claude Opus 4.5 Max at 1501.9.
Score distribution
The chart shows the top 10 models on Arena Elo, with Kimi K1.5 Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 2 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 3 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 4 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 5 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 6 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 7 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 10 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
What this score means
On Arena Elo, Kimi K1.5 Instruct sits at #118 of 600. The leader, Claude Opus 4.5 Max, scores 1501.9, so the gap to the top is 70.1 points. The median model in this index scores 1373.8, which puts Kimi K1.5 Instruct above the midpoint.
Moonshot AI models average 1366.5 on this benchmark. Kimi K1.5 Instruct is 65.3 points above that provider line, so it is a stronger result than the average Moonshot AI model.
Among multimodal models, the average is 1409.6. Kimi K1.5 Instruct is 22.2 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 1431.8 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 $5.15 per million input tokens, Kimi K1.5 Instruct is above the median input price of $4.00 across the 600 models scored on Arena Elo. 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 |
|---|---|---|---|---|
| Doubao 1.5 Pro Turbo | ByteDance | 128000 | $13.81 | 79.4 |
| GPT-4.1 | OpenAI | 1000000 | $15.71 | 82.4 |
| Kimi K2 Base | Moonshot AI | 65536 | $4.82 | 79.2 |
| Kimi K1.5 | Moonshot AI | 128000 | $5.19 | 80.7 |
| Kimi K1.5 Instruct | Moonshot AI | 200000 | $5.15 | 80.5 |
| Llama 4 Scout | Meta AI | 200000 | $4.90 | 79.3 |
| Gemini 3 Flash Vision | Google DeepMind | 400000 | $15.06 | 81.9 |
Other benchmarks for Kimi K1.5 Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 83.0 | 105 | 83th | |
| GPQA Diamond | 77.4 | 98 | 84th | |
| SWE-bench Verified | 67.8 | 151 | 75th | |
| HumanEval | 84.0 | 133 | 78th | |
| MATH-500 | 82.6 | 152 | 75th | |
| AIME 2025 | 80.4 | 83 | 86th | |
| LiveCodeBench | 71.1 | 169 | 72th | |
| IFEval | 86.2 | 150 | 75th | |
| TruthfulQA | 84.3 | 80 | 87th | |
| HellaSwag | 90.2 | 82 | 86th | |
| MMMU | 78.6 | 83 | 86th |
FAQ
What score does Kimi K1.5 Instruct get on Arena Elo?
Kimi K1.5 Instruct scores 1431.8 on Arena Elo in this index. The benchmark average is 1349.9 and the top score is 1501.9.
How does Kimi K1.5 Instruct rank on Arena Elo?
It ranks #118 of 600 models, in the 80th percentile. The leader is Claude Opus 4.5 Max at 1501.9.
Is Kimi K1.5 Instruct good at Arena Elo?
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 Arena Elo score calculated?
Arena Elo is a preference 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 Kimi K1.5 Instruct cost?
Kimi K1.5 Instruct costs $5.15 per million input tokens and $28.20 per million output tokens.