Head-to-head · multimodal vs multimodal

ERNIE 4.5 Reasoning vs Kimi K1.5: which model should you choose?

We compared ERNIE 4.5 Reasoning and Kimi K1.5 on price, context window and 12 benchmarks. Kimi K1.5 has the stronger average benchmark score; Kimi K1.5 is cheaper on a blended 3:1 basis.

Baidu

ERNIE 4.5 Reasoning

ERNIE 4.5 Reasoning is a multimodal model from Baidu. It offers a 1,000,000-token context window, costs $14.36 per million input tokens and $78.75 per million output tokens, and averages 79.8 across the index's benchmark suite.

Input
$14.36 / 1M
Output
$78.75 / 1M
Context
1,000,000
Avg score
79.8
Open weights
No
Full ERNIE 4.5 Reasoning page
Moonshot AI

Kimi K1.5

Kimi K1.5 is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.19 per million input tokens and $21.20 per million output tokens, and averages 80.7 across the index's benchmark suite.

Input
$5.19 / 1M
Output
$21.20 / 1M
Context
128,000
Avg score
80.7
Open weights
Yes
Full Kimi K1.5 page

Specification comparison

SpecERNIE 4.5 ReasoningKimi K1.5Edge
Input price / 1M$14.36$5.19B
Output price / 1M$78.75$21.20B
Context window1,000,000128,000A
Max output8,1924,096A
Average score79.880.7B
Arena Elo1421.51432.2B
Open weightsNoYesB

Benchmark comparison

ERNIE 4.5 Reasoning wins 5, Kimi K1.5 wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkERNIE 4.5 ReasoningKimi K1.5GapWinner
MMLU-Pro80.682.72.1Kimi K1.5
GPQA Diamond75.774.41.3ERNIE 4.5 Reasoning
SWE-bench Verified69.868.21.6ERNIE 4.5 Reasoning
HumanEval85.087.12.1Kimi K1.5
MATH-50082.585.32.8Kimi K1.5
AIME 202578.180.82.7Kimi K1.5
LiveCodeBench71.971.10.8ERNIE 4.5 Reasoning
IFEval88.787.90.8ERNIE 4.5 Reasoning
TruthfulQA82.984.71.8Kimi K1.5
HellaSwag88.087.20.8ERNIE 4.5 Reasoning
MMMU74.477.83.4Kimi K1.5
Arena Elo1421.51432.210.7Kimi K1.5

Price analysis

On a blended 3:1 input-to-output basis, ERNIE 4.5 Reasoning costs about $30.46 per million tokens and Kimi K1.5 costs about $9.19. Kimi K1.5 is roughly 70% cheaper than ERNIE 4.5 Reasoning. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

ERNIE 4.5 Reasoning has the larger context window at 1,000,000 tokens, compared with 128,000 for Kimi K1.5. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, ERNIE 4.5 Reasoning wins 5, Kimi K1.5 wins 7, and 0 are tied. On the index average, Kimi K1.5 leads ERNIE 4.5 Reasoning by 0.9 points (80.7 versus 79.8).

The price picture favours Kimi K1.5. The context picture favours ERNIE 4.5 Reasoning. 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 Kimi K1.5 for quality-sensitive work, and check whether its price holds at your expected volume before committing.

Choose ERNIE 4.5 Reasoning if

  • You need the larger 1,000,000-token context window.
  • You need the larger 8,192-token output limit.
  • The workload needs multi-step reasoning.

Choose Kimi K1.5 if

  • You want the higher average benchmark score in this index.
  • Unit cost matters and you want the cheaper blended price.
  • You need open weights for self-hosting or fine-tuning.

FAQ

Is ERNIE 4.5 Reasoning better than Kimi K1.5?

On the index average, Kimi K1.5 leads ERNIE 4.5 Reasoning by 0.9 points (80.7 versus 79.8). The better choice depends on price, context and your task. ERNIE 4.5 Reasoning wins 5 benchmarks, Kimi K1.5 wins 7.

Which is cheaper, ERNIE 4.5 Reasoning or Kimi K1.5?

Kimi K1.5 is cheaper on a blended 3:1 basis at about $9.19 per million tokens, versus $30.46 for ERNIE 4.5 Reasoning.

Which has the larger context window?

ERNIE 4.5 Reasoning has the larger context window at 1,000,000 tokens. Kimi K1.5 has 128,000 tokens.

Are these models open weights?

ERNIE 4.5 Reasoning is proprietary; Kimi K1.5 is open weights.

Which should I choose for production?

If you need a single default, start with Kimi K1.5 for quality-sensitive work, and check whether its price holds at your expected volume before committing.

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