Head-to-head · multimodal vs multimodal

Doubao 1.5 Pro Turbo vs Kimi K1.5 Chat: which model should you choose?

We compared Doubao 1.5 Pro Turbo and Kimi K1.5 Chat on price, context window and 12 benchmarks. Kimi K1.5 Chat has the stronger average benchmark score; Kimi K1.5 Chat is cheaper on a blended 3:1 basis.

ByteDance

Doubao 1.5 Pro Turbo

Doubao 1.5 Pro Turbo is a multimodal model from ByteDance. It offers a 128,000-token context window, costs $13.81 per million input tokens and $71.10 per million output tokens, and averages 79.4 across the index's benchmark suite.

Input
$13.81 / 1M
Output
$71.10 / 1M
Context
128,000
Avg score
79.4
Open weights
No
Full Doubao 1.5 Pro Turbo page
Moonshot AI

Kimi K1.5 Chat

Kimi K1.5 Chat is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.11 per million input tokens and $26.59 per million output tokens, and averages 80.2 across the index's benchmark suite.

Input
$5.11 / 1M
Output
$26.59 / 1M
Context
128,000
Avg score
80.2
Open weights
Yes
Full Kimi K1.5 Chat page

Specification comparison

SpecDoubao 1.5 Pro TurboKimi K1.5 ChatEdge
Input price / 1M$13.81$5.11B
Output price / 1M$71.10$26.59B
Context window128,000128,000Tie
Max output65,53632,768A
Average score79.480.2B
Arena Elo1433.01422.6A
Open weightsNoYesB

Benchmark comparison

Doubao 1.5 Pro Turbo wins 6, Kimi K1.5 Chat wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkDoubao 1.5 Pro TurboKimi K1.5 ChatGapWinner
MMLU-Pro80.381.31.0Kimi K1.5 Chat
GPQA Diamond75.478.12.7Kimi K1.5 Chat
SWE-bench Verified67.769.92.2Kimi K1.5 Chat
HumanEval85.084.30.7Doubao 1.5 Pro Turbo
MATH-50081.481.70.3Kimi K1.5 Chat
AIME 202575.681.45.8Kimi K1.5 Chat
LiveCodeBench72.375.73.4Kimi K1.5 Chat
IFEval88.988.20.7Doubao 1.5 Pro Turbo
TruthfulQA80.980.30.6Doubao 1.5 Pro Turbo
HellaSwag88.287.21.0Doubao 1.5 Pro Turbo
MMMU77.673.64.0Doubao 1.5 Pro Turbo
Arena Elo1433.01422.610.4Doubao 1.5 Pro Turbo

Price analysis

On a blended 3:1 input-to-output basis, Doubao 1.5 Pro Turbo costs about $28.13 per million tokens and Kimi K1.5 Chat costs about $10.48. Kimi K1.5 Chat is roughly 63% cheaper than Doubao 1.5 Pro Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Doubao 1.5 Pro Turbo wins 6, Kimi K1.5 Chat wins 6, and 0 are tied. On the index average, Kimi K1.5 Chat leads Doubao 1.5 Pro Turbo by 0.8 points (80.2 versus 79.4).

The price picture favours Kimi K1.5 Chat. The context picture is effectively a tie. 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 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.

Choose Doubao 1.5 Pro Turbo if

  • You need the larger 65,536-token output limit.

Choose Kimi K1.5 Chat 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 Doubao 1.5 Pro Turbo better than Kimi K1.5 Chat?

On the index average, Kimi K1.5 Chat leads Doubao 1.5 Pro Turbo by 0.8 points (80.2 versus 79.4). The better choice depends on price, context and your task. Doubao 1.5 Pro Turbo wins 6 benchmarks, Kimi K1.5 Chat wins 6.

Which is cheaper, Doubao 1.5 Pro Turbo or Kimi K1.5 Chat?

Kimi K1.5 Chat is cheaper on a blended 3:1 basis at about $10.48 per million tokens, versus $28.13 for Doubao 1.5 Pro Turbo.

Which has the larger context window?

Both models list a 128,000-token context window.

Are these models open weights?

Doubao 1.5 Pro Turbo is proprietary; Kimi K1.5 Chat is open weights.

Which should I choose for production?

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

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