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Comparison · 2 models · Updated Oct 4, 2026

Kimi K2 Thinking vs Llama 4 Scout 17B Instruct

Llama 4 Scout 17B Instruct comes out ahead, 62 to 58 on our weighted score, and it is the cheaper option too.

  1. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    58/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
    Full specs Remove Kimi K2 Thinking
  2. Our pick

    Meta

    Llama 4 Scout 17B Instruct

    Released Apr 5, 2025

    62/100
    • ECI129.7
    • Price$0.225 / $0.69
    • Context10M
    Full specs Remove Llama 4 Scout 17B Instruct

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Llama 4 Scout 17B Instruct is our pick

Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Kimi K2 Thinking (58). It leads on price, inputs & features and context window. Kimi K2 Thinking wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · Llama 4 Scout 17B Instruct 129.7
  • Lowest priceLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct $0.341 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · Kimi K2 Thinking 262,144 tokens
  • Widest inputsLlama 4 Scout 17B InstructKimi K2 Thinking: Text · Llama 4 Scout 17B Instruct: Text, Images
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightKimi K2 ThinkingLlama 4 Scout 17B Instruct
CapabilityCapabilities Index (ECI)50%7352
Price25%4872
Inputs & features15%3550
Context window10%37100
Overall100%58/10062/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Kimi K2 Thinking vs Llama 4 Scout 17B Instruct specifications side by side
SpecificationKimi K2 ThinkingMoonshot AILlama 4 Scout 17B InstructMeta
Capability
Capabilities Index (ECI)146.0 (best)129.7
ECI rank#72 of 148 (best)#126 of 148
GPQA DiamondGraduate-level science questions84.2% (best)51.8%
OTIS Mock AIME 2024–2025Competition mathematics83.1% (best)7.8%
Price per million tokens
Input$0.60$0.225 (best)
Output$2.50$0.69 (best)
Cached input——
Blended (3:1)$1.07$0.341 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 10 providersMedian of 4 providers
Limits
Context window262,144 tokens10,000,000 tokens (best)
Max output262,144 tokens (best)16,384 tokens
Inputs and features
TextYesYes
ImagesNoYes
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model ID——
API providers10 (best)4
ReleasedNov 6, 2025Apr 5, 2025
Knowledge cutoffAug 2024Aug 2024
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Kimi K2 Thinking$11.00
  • Llama 4 Scout 17B Instruct$3.63
04 — Questions

Which should you choose?

Which is better: Kimi K2 Thinking or Llama 4 Scout 17B Instruct?

Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Kimi K2 Thinking (58). It leads on price, inputs & features and context window. Kimi K2 Thinking wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2 Thinking or Llama 4 Scout 17B Instruct?

Llama 4 Scout 17B Instruct is cheaper at $0.225 input / $0.69 output per million tokens (median across 4 API providers). Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.341 per million tokens for Llama 4 Scout 17B Instruct versus $1.07 for Kimi K2 Thinking (3.1× as much).

Which scores higher on benchmarks?

Kimi K2 Thinking scores higher on the Capabilities Index (ECI): Kimi K2 Thinking 146.0 (#72 of 148) and Llama 4 Scout 17B Instruct 129.7 (#126 of 148). Their confidence ranges do not overlap (143.4–147.6 vs 124.8–131.4), so the gap is a real one. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, Llama 4 Scout 17B Instruct 51.8%; OTIS Mock AIME 2024–2025 — Kimi K2 Thinking 83.1%, Llama 4 Scout 17B Instruct 7.8%.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2 Thinking and Llama 4 Scout 17B Instruct yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Thinking leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Which has the bigger context window?

Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 tokens, against 262,144 for Kimi K2 Thinking. Maximum output per response: Kimi K2 Thinking up to 262,144, Llama 4 Scout 17B Instruct up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Kimi K2 Thinking accepts text; Llama 4 Scout 17B Instruct accepts text and images. Llama 4 Scout 17B Instruct handles the widest range of inputs.

Are any of these open source?

Yes, both publish their weights, so you can self-host them.

Which is newer?

Kimi K2 Thinking is the newest, released Nov 6, 2025. Llama 4 Scout 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, Llama 4 Scout 17B Instruct Aug 2024.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.

05 — Keep comparing

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