Model A

OpenAI

73.27/100

Supported · Public rank #9

90% interval 71.0–75.6

GPT-5.5 vs Llama 4 Scout

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Llama 4 Scout

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Llama 4 Scout
Llama 4 Scout has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Llama 4 Scout is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Llama 4 Scout is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

37 GPT-5.5 only

1 Shared

Shared results: 1
GPT-5.5 only: 37
Llama 4 Scout only: 0
Like-for-like categories: 0 / 8

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only

GPT-5.5: 63.9; Supported · #15/151
Llama 4 Scout: 33.1; Estimated · #134/151
Basis: BenchAlign lane · 13 vs 0 public rows
Reading: Directional only

Coding

Directional only

GPT-5.5: 67.7; Supported · #8/183
Llama 4 Scout: 32.9; Estimated · #160/183
Basis: BenchAlign lane · 9 vs 0 public rows
Reading: Directional only

Knowledge

Directional only

GPT-5.5: 73.3; Supported · #7/181
Llama 4 Scout: 34.1; Estimated · #169/181
Basis: BenchAlign lane · 6 vs 0 public rows
Reading: Directional only

Instruction following

Directional only

GPT-5.5: 92.9; #7/120
Llama 4 Scout: 45.7; #89/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.5: 63.5; #15/22
Llama 4 Scout: 42.8; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.5: 69.6; Unranked · 3 rankable rows
Llama 4 Scout: 25.3; Unranked · 1 rankable row
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.5: Not ranked
Llama 4 Scout: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.5: 71.3; #19/48
Llama 4 Scout: 38.0; Unranked · 1 rankable row
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.5 and Llama 4 Scout
Category	GPT-5.5	Llama 4 Scout	Basis	Reading
Agentic	63.9Supported · #15/151	33.1Estimated · #134/151	Directional onlyBenchAlign lane · 13 vs 0 public rows	Directional only
Coding	67.7Supported · #8/183	32.9Estimated · #160/183	Directional onlyBenchAlign lane · 9 vs 0 public rows	Directional only
Knowledge	73.3Supported · #7/181	34.1Estimated · #169/181	Directional onlyBenchAlign lane · 6 vs 0 public rows	Directional only
Instruction following	92.9#7/120	45.7#89/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	63.5#15/22	42.8Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Math	69.6Unranked · 3 rankable rows	25.3Unranked · 1 rankable row	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	71.3#19/48	38.0Unranked · 1 rankable row	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

FrontierMath v2 (Tiers 1-3)
Math
GPT-5.5: 51.700%Llama 4 Scout: 0.000%Normalized gap 51.7Shared source

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-5.5: $0.02; Fits in one request
Llama 4 Scout: Self-hosted; infrastructure cost varies; Fits in one request

Llama 4 Scout has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.5: $0.34; Fits in one request
Llama 4 Scout: Self-hosted; infrastructure cost varies; Fits in one request

Llama 4 Scout has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-5.5: $0.5; Fits in one request
Llama 4 Scout: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Llama 4 Scout has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGPT-5.5Llama 4 Scout

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.5

OpenAI pricing

Llama 4 Scout

10M

API model ID

GPT-5.5

gpt-5.5

OpenAI pricing

Llama 4 Scout

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

Llama 4 Scout

No comparable hosted API rate

Documented inputs

GPT-5.5

Not sourced

Llama 4 Scout

Not sourced

Documented outputs

GPT-5.5

Not sourced

Llama 4 Scout

Not sourced

Provider availability

GPT-5.5

Not sourced

Llama 4 Scout

Not sourced

Reasoning profile

GPT-5.5

Reasoning

Llama 4 Scout

Non-Reasoning

Weight access

GPT-5.5

Proprietary

Llama 4 Scout

Open Weight

License

GPT-5.5

Proprietary

Llama 4 Scout

Open Weight

Release date

GPT-5.5

2026-04-23

Llama 4 Scout

2026-02-28

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: GPT-5.5 has the higher public score, 73.27 versus 35.64, and the 90% score intervals do not overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Llama 4 Scout has the larger documented window (10M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-5.5

API / mo$26,250

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Llama 4 Scout

API / mo$0

Self-host / mo$2,278

Break-even—

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence38 rows

Agentic

Terminal-Bench 2.0
GPT-5.582%
Source
Llama 4 Scout—
Not directly comparable
CyberGym
GPT-5.581.8%
Source
Llama 4 Scout—
Not directly comparable
BrowseComp
GPT-5.584.4%
Source
Llama 4 Scout—
Not directly comparable
OSWorld-Verified
GPT-5.578.7%
Source
Llama 4 Scout—
Not directly comparable
MCP Atlas
GPT-5.575.3%
Source
Llama 4 Scout—
Not directly comparable
Toolathlon
GPT-5.555.6%
Source
Llama 4 Scout—
Not directly comparable
τ²-bench results
GPT-5.598%
Source
Llama 4 Scout—
Not directly comparable
Gert Labs
GPT-5.572.93%
Source
Llama 4 Scout—
Not directly comparable
ResearchClawBench
GPT-5.517.0%
Source
Llama 4 Scout—
Not directly comparable
OSWorld 2.0
GPT-5.513.0%
Source
Llama 4 Scout—
Not directly comparable
JobBench
GPT-5.542.7%
Source
Llama 4 Scout—
Not directly comparable
ExploitGym
GPT-5.513.4%
Source
Llama 4 Scout—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%
Source
Llama 4 Scout—
Not directly comparable

Coding

SWE-bench Pro
GPT-5.558.6%
Source
Llama 4 Scout—
Not directly comparable
Terminal-Bench 2.0
GPT-5.582.0%
Source
Llama 4 Scout—
Not directly comparable
Vibe Code Bench
GPT-5.569.85%
Source
Llama 4 Scout—
Not directly comparable
React Native Evals
GPT-5.584.7%
Source
Llama 4 Scout—
Not directly comparable
cursorBench31
GPT-5.559.2%
Source
Llama 4 Scout—
Not directly comparable
cursorBench32
GPT-5.558.4%
Source
Llama 4 Scout—
Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%
Source
Llama 4 Scout—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%
Source
Llama 4 Scout—
Not directly comparable
SWE-bench (Vals)
GPT-5.582.6%
Source
Llama 4 Scout—
Not directly comparable

Reasoning

MRCR v2 64K-128K
GPT-5.583.1%
Source
Llama 4 Scout—
Not directly comparable
MRCR v2 128K-256K
GPT-5.587.5%
Source
Llama 4 Scout—
Not directly comparable
ARC-AGI-2
GPT-5.585%
Source
Llama 4 Scout—
Not directly comparable
ARC-AGI-3
GPT-5.50.4%
Source
Llama 4 Scout—
Not directly comparable

Knowledge

GPQA
GPT-5.593.6%
Source
Llama 4 Scout—
Not directly comparable
GPQA-D
GPT-5.593.6%
Source
Llama 4 Scout—
Not directly comparable
HLE
GPT-5.552.2%
Source
Llama 4 Scout—
Not directly comparable
HLE w/o tools
GPT-5.541.4%
Source
Llama 4 Scout—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%
Source
Llama 4 Scout—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.588.1%
Source
Llama 4 Scout—
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.551.7%
Source
Llama 4 Scout—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared source
GPT-5.551.700%
Llama 4 Scout0.000%
GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
GPT-5.535.400%
Source
Llama 4 Scout—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.581.2%
Source
Llama 4 Scout—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.583.2%
Source
Llama 4 Scout—
Not directly comparable
OfficeQA Pro
GPT-5.554.1%
Source
Llama 4 Scout—
Not directly comparable

Frequently asked questions

Which is better, GPT-5.5 or Llama 4 Scout?

GPT-5.5 has the higher public score, 73.27 versus 35.64, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.5 or Llama 4 Scout?

GPT-5.5 scores higher for coding on the public lane, 67.7 to 32.9. Llama 4 Scout is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-5.5 or Llama 4 Scout?

GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 33.1. Llama 4 Scout is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.5 or Llama 4 Scout?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.5 or Llama 4 Scout?

Llama 4 Scout has the larger documented context window: 10M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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GPT-5.5 vs Llama 4 Scout

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GPT-5.5 or Llama 4 Scout?

Which is better for coding, GPT-5.5 or Llama 4 Scout?

Which is better for agentic tasks, GPT-5.5 or Llama 4 Scout?

Which costs less, GPT-5.5 or Llama 4 Scout?

Which has the larger context window, GPT-5.5 or Llama 4 Scout?

Related comparisons

Watch GPT-5.5 vs Llama 4 Scout