Head-to-head · frontier vs multimodal

GPT-4.1 Turbo vs Kimi K2 Chat: which model should you choose?

We compared GPT-4.1 Turbo and Kimi K2 Chat on price, context window and 12 benchmarks. GPT-4.1 Turbo has the stronger average benchmark score; Kimi K2 Chat is cheaper on a blended 3:1 basis.

OpenAI

GPT-4.1 Turbo

GPT-4.1 Turbo is a frontier model from OpenAI. It offers a 1,000,000-token context window, costs $16.12 per million input tokens and $69.91 per million output tokens, and averages 82.9 across the index's benchmark suite.

Input
$16.12 / 1M
Output
$69.91 / 1M
Context
1,000,000
Avg score
82.9
Open weights
No
Full GPT-4.1 Turbo page
Moonshot AI

Kimi K2 Chat

Kimi K2 Chat is a multimodal model from Moonshot AI. It offers a 400,000-token context window, costs $5.74 per million input tokens and $22.24 per million output tokens, and averages 82.1 across the index's benchmark suite.

Input
$5.74 / 1M
Output
$22.24 / 1M
Context
400,000
Avg score
82.1
Open weights
Yes
Full Kimi K2 Chat page

Specification comparison

SpecGPT-4.1 TurboKimi K2 ChatEdge
Input price / 1M$16.12$5.74B
Output price / 1M$69.91$22.24B
Context window1,000,000400,000A
Max output128,0004,096A
Average score82.982.1A
Arena Elo1446.21445.6A
Open weightsNoYesB

Benchmark comparison

GPT-4.1 Turbo wins 8, Kimi K2 Chat wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkGPT-4.1 TurboKimi K2 ChatGapWinner
MMLU-Pro84.682.71.9GPT-4.1 Turbo
GPQA Diamond76.177.41.3Kimi K2 Chat
SWE-bench Verified74.770.04.7GPT-4.1 Turbo
HumanEval89.888.11.7GPT-4.1 Turbo
MATH-50086.984.82.1GPT-4.1 Turbo
AIME 202577.484.16.7Kimi K2 Chat
LiveCodeBench78.774.54.2GPT-4.1 Turbo
IFEval88.288.00.2GPT-4.1 Turbo
TruthfulQA87.082.84.2GPT-4.1 Turbo
HellaSwag90.091.61.6Kimi K2 Chat
MMMU78.579.61.1Kimi K2 Chat
Arena Elo1446.21445.60.6GPT-4.1 Turbo

Price analysis

On a blended 3:1 input-to-output basis, GPT-4.1 Turbo costs about $29.57 per million tokens and Kimi K2 Chat costs about $9.86. Kimi K2 Chat is roughly 67% cheaper than GPT-4.1 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

GPT-4.1 Turbo has the larger context window at 1,000,000 tokens, compared with 400,000 for Kimi K2 Chat. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, GPT-4.1 Turbo wins 8, Kimi K2 Chat wins 4, and 0 are tied. On the index average, GPT-4.1 Turbo leads Kimi K2 Chat by 0.8 points (82.9 versus 82.1).

The price picture favours Kimi K2 Chat. The context picture favours GPT-4.1 Turbo. 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 GPT-4.1 Turbo for quality-sensitive work and Kimi K2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose GPT-4.1 Turbo if

  • You want the higher average benchmark score in this index.
  • You need the larger 1,000,000-token context window.
  • You need the larger 128,000-token output limit.

Choose Kimi K2 Chat if

  • Unit cost matters and you want the cheaper blended price.
  • You need open weights for self-hosting or fine-tuning.

FAQ

Is GPT-4.1 Turbo better than Kimi K2 Chat?

On the index average, GPT-4.1 Turbo leads Kimi K2 Chat by 0.8 points (82.9 versus 82.1). The better choice depends on price, context and your task. GPT-4.1 Turbo wins 8 benchmarks, Kimi K2 Chat wins 4.

Which is cheaper, GPT-4.1 Turbo or Kimi K2 Chat?

Kimi K2 Chat is cheaper on a blended 3:1 basis at about $9.86 per million tokens, versus $29.57 for GPT-4.1 Turbo.

Which has the larger context window?

GPT-4.1 Turbo has the larger context window at 1,000,000 tokens. Kimi K2 Chat has 400,000 tokens.

Are these models open weights?

GPT-4.1 Turbo is proprietary; Kimi K2 Chat is open weights.

Which should I choose for production?

If you need a single default, start with GPT-4.1 Turbo for quality-sensitive work and Kimi K2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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