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Comparison · 2 models · Updated Oct 4, 2026

Llama 4 Scout 17B Instruct vs GPT-5.4 nano

GPT-5.4 nano comes out ahead, 68 to 62 on our weighted score, though Llama 4 Scout 17B Instruct is 26% cheaper per token.

  1. 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
  2. Our pick

    OpenAI

    GPT-5.4 nano

    Released Mar 17, 2026

    68/100
    • ECI145.8
    • Price$0.20 / $1.25
    • Context400K
    Full specs Remove GPT-5.4 nano

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

GPT-5.4 nano is our pick

GPT-5.4 nano is the better all-round choice, scoring 68/100 against Llama 4 Scout 17B Instruct (62). It leads on capability and inputs & features. Llama 4 Scout 17B Instruct wins on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGPT-5.4 nanoCapabilities Index (ECI): GPT-5.4 nano 145.8 · Llama 4 Scout 17B Instruct 129.7
  • Lowest priceLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct $0.341 · GPT-5.4 nano $0.463 per 1M tokens (3:1 blend)
  • Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · GPT-5.4 nano 400,000 tokens
  • Widest inputsSame inputsLlama 4 Scout 17B Instruct: Text, Images · GPT-5.4 nano: Text, Images
  • Self-hostingLlama 4 Scout 17B InstructPublishes downloadable weights
How the score is built
MeasureWeightLlama 4 Scout 17B InstructGPT-5.4 nano
CapabilityCapabilities Index (ECI)50%5273
Price25%7266
Inputs & features15%5070
Context window10%10044
Overall100%62/10068/100
02 — Side by side

Every spec in one table

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

Llama 4 Scout 17B Instruct vs GPT-5.4 nano specifications side by side
SpecificationLlama 4 Scout 17B InstructMetaGPT-5.4 nanoOpenAI
Capability
Capabilities Index (ECI)129.7145.8 (best)
ECI rank#126 of 148#75 of 148 (best)
GPQA DiamondGraduate-level science questions51.8%78.5% (best)
FrontierMath Tiers 1–3Research-level mathematics—44.9%
OTIS Mock AIME 2024–2025Competition mathematics7.8%87.8% (best)
SimpleQA VerifiedShort factual questions—11.7%
Price per million tokens
Input$0.225$0.20 (best)
Output$0.69 (best)$1.25
Cached input—$0.02
Blended (3:1)$0.341 (best)$0.463
Long-context rateSame rateSame rate
Price sourceMedian of 4 providersOfficial OpenAI API
Limits
Context window10,000,000 tokens (best)400,000 tokens
Max output16,384 tokens128,000 tokens (best)
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoYeslow · medium · high · xhigh
Tool callingYesYes
Structured outputNoYes
Availability
WeightsOpenProprietary
API model ID—gpt-5.4-nano
API providers426 (best)
ReleasedApr 5, 2025Mar 17, 2026
Knowledge cutoffAug 2024Aug 31, 2025
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.

  • Llama 4 Scout 17B Instruct$3.63
  • GPT-5.4 nano$4.50
04 — Questions

Which should you choose?

Which is better: Llama 4 Scout 17B Instruct or GPT-5.4 nano?

GPT-5.4 nano is the better all-round choice, scoring 68/100 against Llama 4 Scout 17B Instruct (62). It leads on capability and inputs & features. Llama 4 Scout 17B Instruct wins on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Llama 4 Scout 17B Instruct or GPT-5.4 nano?

Llama 4 Scout 17B Instruct is cheaper at $0.225 input / $0.69 output per million tokens (median across 4 API providers). GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI API price). 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 $0.463 for GPT-5.4 nano (1.4× as much).

Which scores higher on benchmarks?

GPT-5.4 nano scores higher on the Capabilities Index (ECI): GPT-5.4 nano 145.8 (#75 of 148) and Llama 4 Scout 17B Instruct 129.7 (#126 of 148). Their confidence ranges do not overlap (143.2–147.7 vs 124.8–131.4), so the gap is a real one. On individual benchmarks: GPQA Diamond — GPT-5.4 nano 78.5%, Llama 4 Scout 17B Instruct 51.8%; OTIS Mock AIME 2024–2025 — GPT-5.4 nano 87.8%, Llama 4 Scout 17B Instruct 7.8%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama 4 Scout 17B Instruct and GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, GPT-5.4 nano 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 400,000 for GPT-5.4 nano. Maximum output per response: Llama 4 Scout 17B Instruct up to 16,384, GPT-5.4 nano up to 128,000 tokens.

Which can read images, PDFs, audio or video?

Llama 4 Scout 17B Instruct accepts text and images; GPT-5.4 nano accepts text and images. They handle the same number of input types.

Are any of these open source?

Llama 4 Scout 17B Instruct publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

Which is newer?

GPT-5.4 nano is the newest, released Mar 17, 2026. Llama 4 Scout 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Llama 4 Scout 17B Instruct Aug 2024, GPT-5.4 nano Aug 31, 2025.

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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