ERNIE 4.5 Max vs Kimi K1.5 Instruct: which model should you choose?
We compared ERNIE 4.5 Max and Kimi K1.5 Instruct on price, context window and 12 benchmarks. ERNIE 4.5 Max has the stronger average benchmark score; Kimi K1.5 Instruct is cheaper on a blended 3:1 basis.
ERNIE 4.5 Max
ERNIE 4.5 Max is a multimodal model from Baidu. It offers a 128,000-token context window, costs $14.41 per million input tokens and $53.71 per million output tokens, and averages 80.6 across the index's benchmark suite.
- Input
- $14.41 / 1M
- Output
- $53.71 / 1M
- Context
- 128,000
- Avg score
- 80.6
- Open weights
- No
Kimi K1.5 Instruct
Kimi K1.5 Instruct is a multimodal model from Moonshot AI. It offers a 200,000-token context window, costs $5.15 per million input tokens and $28.20 per million output tokens, and averages 80.5 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $28.20 / 1M
- Context
- 200,000
- Avg score
- 80.5
- Open weights
- Yes
Specification comparison
| Spec | ERNIE 4.5 Max | Kimi K1.5 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $14.41 | $5.15 | B |
| Output price / 1M | $53.71 | $28.20 | B |
| Context window | 128,000 | 200,000 | B |
| Max output | 65,536 | 4,096 | A |
| Average score | 80.6 | 80.5 | A |
| Arena Elo | 1423.7 | 1431.8 | B |
| Open weights | No | Yes | B |
Benchmark comparison
ERNIE 4.5 Max wins 5, Kimi K1.5 Instruct wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | ERNIE 4.5 Max | Kimi K1.5 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.8 | 83.0 | 2.2 | Kimi K1.5 Instruct |
| GPQA Diamond | 76.4 | 77.4 | 1.0 | Kimi K1.5 Instruct |
| SWE-bench Verified | 68.9 | 67.8 | 1.1 | ERNIE 4.5 Max |
| HumanEval | 87.7 | 84.0 | 3.7 | ERNIE 4.5 Max |
| MATH-500 | 84.2 | 82.6 | 1.6 | ERNIE 4.5 Max |
| AIME 2025 | 79.5 | 80.4 | 0.9 | Kimi K1.5 Instruct |
| LiveCodeBench | 73.0 | 71.1 | 1.9 | ERNIE 4.5 Max |
| IFEval | 87.1 | 86.2 | 0.9 | ERNIE 4.5 Max |
| TruthfulQA | 83.6 | 84.3 | 0.7 | Kimi K1.5 Instruct |
| HellaSwag | 87.7 | 90.2 | 2.5 | Kimi K1.5 Instruct |
| MMMU | 78.1 | 78.6 | 0.5 | Kimi K1.5 Instruct |
| Arena Elo | 1423.7 | 1431.8 | 8.1 | Kimi K1.5 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, ERNIE 4.5 Max costs about $24.23 per million tokens and Kimi K1.5 Instruct costs about $10.91. Kimi K1.5 Instruct is roughly 55% cheaper than ERNIE 4.5 Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Kimi K1.5 Instruct has the larger context window at 200,000 tokens, compared with 128,000 for ERNIE 4.5 Max. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, ERNIE 4.5 Max wins 5, Kimi K1.5 Instruct wins 7, and 0 are tied. On the index average, ERNIE 4.5 Max leads Kimi K1.5 Instruct by 0.1 points (80.6 versus 80.5).
The price picture favours Kimi K1.5 Instruct. The context picture favours Kimi K1.5 Instruct. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with ERNIE 4.5 Max for quality-sensitive work and Kimi K1.5 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose ERNIE 4.5 Max if
- You want the higher average benchmark score in this index.
- You need the larger 65,536-token output limit.
Choose Kimi K1.5 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 200,000-token context window.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is ERNIE 4.5 Max better than Kimi K1.5 Instruct?
On the index average, ERNIE 4.5 Max leads Kimi K1.5 Instruct by 0.1 points (80.6 versus 80.5). The better choice depends on price, context and your task. ERNIE 4.5 Max wins 5 benchmarks, Kimi K1.5 Instruct wins 7.
Which is cheaper, ERNIE 4.5 Max or Kimi K1.5 Instruct?
Kimi K1.5 Instruct is cheaper on a blended 3:1 basis at about $10.91 per million tokens, versus $24.23 for ERNIE 4.5 Max.
Which has the larger context window?
Kimi K1.5 Instruct has the larger context window at 200,000 tokens. ERNIE 4.5 Max has 128,000 tokens.
Are these models open weights?
ERNIE 4.5 Max is proprietary; Kimi K1.5 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with ERNIE 4.5 Max for quality-sensitive work and Kimi K1.5 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.