Head-to-head · multimodal vs multimodal

Kimi K1.5 Chat vs Llama 4 Maverick Chat: which model should you choose?

We compared Kimi K1.5 Chat and Llama 4 Maverick Chat on price, context window and 12 benchmarks. Kimi K1.5 Chat has the stronger average benchmark score; Llama 4 Maverick Chat is cheaper on a blended 3:1 basis.

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
Meta AI

Llama 4 Maverick Chat

Llama 4 Maverick Chat is a multimodal model from Meta AI. It offers a 128,000-token context window, costs $5.09 per million input tokens and $23.20 per million output tokens, and averages 79.7 across the index's benchmark suite.

Input
$5.09 / 1M
Output
$23.20 / 1M
Context
128,000
Avg score
79.7
Open weights
Yes
Full Llama 4 Maverick Chat page

Specification comparison

SpecKimi K1.5 ChatLlama 4 Maverick ChatEdge
Input price / 1M$5.11$5.09B
Output price / 1M$26.59$23.20B
Context window128,000128,000Tie
Max output32,76832,768Tie
Average score80.279.7A
Arena Elo1422.61419.9A
Open weightsYesYesTie

Benchmark comparison

Kimi K1.5 Chat wins 7, Llama 4 Maverick Chat wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkKimi K1.5 ChatLlama 4 Maverick ChatGapWinner
MMLU-Pro81.380.50.8Kimi K1.5 Chat
GPQA Diamond78.173.05.1Kimi K1.5 Chat
SWE-bench Verified69.966.63.3Kimi K1.5 Chat
HumanEval84.383.21.1Kimi K1.5 Chat
MATH-50081.784.83.1Llama 4 Maverick Chat
AIME 202581.478.92.5Kimi K1.5 Chat
LiveCodeBench75.776.30.6Llama 4 Maverick Chat
IFEval88.287.40.8Kimi K1.5 Chat
TruthfulQA80.381.51.2Llama 4 Maverick Chat
HellaSwag87.288.81.6Llama 4 Maverick Chat
MMMU73.675.41.8Llama 4 Maverick Chat
Arena Elo1422.61419.92.7Kimi K1.5 Chat

Price analysis

On a blended 3:1 input-to-output basis, Kimi K1.5 Chat costs about $10.48 per million tokens and Llama 4 Maverick Chat costs about $9.62. Llama 4 Maverick Chat is roughly 8% cheaper than Kimi K1.5 Chat. 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, Kimi K1.5 Chat wins 7, Llama 4 Maverick Chat wins 5, and 0 are tied. On the index average, Kimi K1.5 Chat leads Llama 4 Maverick Chat by 0.5 points (80.2 versus 79.7).

The price picture favours Llama 4 Maverick 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 Llama 4 Maverick Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose Kimi K1.5 Chat if

  • You want the higher average benchmark score in this index.

Choose Llama 4 Maverick Chat if

  • Unit cost matters and you want the cheaper blended price.

FAQ

Is Kimi K1.5 Chat better than Llama 4 Maverick Chat?

On the index average, Kimi K1.5 Chat leads Llama 4 Maverick Chat by 0.5 points (80.2 versus 79.7). The better choice depends on price, context and your task. Kimi K1.5 Chat wins 7 benchmarks, Llama 4 Maverick Chat wins 5.

Which is cheaper, Kimi K1.5 Chat or Llama 4 Maverick Chat?

Llama 4 Maverick Chat is cheaper on a blended 3:1 basis at about $9.62 per million tokens, versus $10.48 for Kimi K1.5 Chat.

Which has the larger context window?

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

Are these models open weights?

Kimi K1.5 Chat is open weights; Llama 4 Maverick 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 Llama 4 Maverick 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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