Long documents
Prompts that approach the documented context limit
Llama 4 Scout
Llama 4 Scout has the larger documented context window.
Confidence: documented
GPT-5.4 vs Llama 4 Scout
Updated October 17, 2026. Rank says GPT-5.4 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- GPT-5.4 only
- 42
- Llama 4 Scout only
- 0
- Like-for-like categories
- 0 / 8
Supported: GPT-5.4 and Llama 4 ScoutHow the comparison works
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.
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: limitedAgentic 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: limitedChat 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-ratesCache-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-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
47.7GPT-5.49.0Llama 4 Scout
Directional only · BenchAlign v5.8
GPT-5.4 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
42 GPT-5.4 only
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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-5.4
- 52.1
- Supported · #44/128
- Llama 4 Scout
- 6.8
- Estimated · #125/128
- Basis
- BenchAlign v5.8 lane · 15 vs 0 public rows
- Reading
- Directional only
Coding
Directional only- GPT-5.4
- 47.7
- Supported · #48/150
- Llama 4 Scout
- 9.0
- Estimated · #147/150
- Basis
- BenchAlign v5.8 lane · 6 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.4
- 69.1
- Supported · #20/184
- Llama 4 Scout
- 26.6
- Estimated · #167/184
- Basis
- BenchAlign v5.8 lane · 7 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.4
- 90.7
- #23/148
- Llama 4 Scout
- 45.8
- #104/148
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.4
- 64.2
- #23/28
- Llama 4 Scout
- 44.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4
- 73.1
- #30/54
- Llama 4 Scout
- 42.5
- Unranked · 1 rankable row
- Basis
- Provisional lane · 3 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4
- Not ranked
- Llama 4 Scout
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4
- 82.0
- Unranked · 2 rankable rows
- Llama 4 Scout
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.4 | Llama 4 Scout | Basis | Reading |
|---|---|---|---|---|
| Agentic | 52.1Supported · #44/128 | 6.8Estimated · #125/128 | Directional onlyBenchAlign v5.8 lane · 15 vs 0 public rows | Directional only |
| Coding | 47.7Supported · #48/150 | 9.0Estimated · #147/150 | Directional onlyBenchAlign v5.8 lane · 6 vs 0 public rows | Directional only |
| Knowledge | 69.1Supported · #20/184 | 26.6Estimated · #167/184 | Directional onlyBenchAlign v5.8 lane · 7 vs 0 public rows | Directional only |
| Instruction following | 90.7#23/148 | 45.8#104/148 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 64.2#23/28 | 44.9Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 73.1#30/54 | 42.5Unranked · 1 rankable row | Not comparableProvisional lane · 3 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 82.0Unranked · 2 rankable rows | Not ranked | 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 v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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-5.4
- $0.01
- 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-5.4
- $0.17
- 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-5.4
- $0.25
- 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.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.4Llama 4 Scout
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4
1.05M
OpenAI pricingLlama 4 Scout
10M
API model ID
GPT-5.4
gpt-5.4
OpenAI pricingLlama 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-5.4
$0.25 per 1M cached input tokens
OpenAI pricingLlama 4 Scout
No comparable hosted API rate
Documented inputs
GPT-5.4
Not sourced
Llama 4 Scout
Not sourced
Documented outputs
GPT-5.4
Not sourced
Llama 4 Scout
Not sourced
Provider availability
GPT-5.4
Not sourced
Llama 4 Scout
Not sourced
Reasoning profile
GPT-5.4
Reasoning
Llama 4 Scout
Non-Reasoning
Weight access
GPT-5.4
Proprietary
Llama 4 Scout
Open Weight
License
GPT-5.4
Proprietary
Llama 4 Scout
Open Weight
Release date
GPT-5.4
2026-03-05
Llama 4 Scout
2025-04-05
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- 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.
Questions
Which is better, GPT-5.4 or Llama 4 Scout?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.4 or Llama 4 Scout?
GPT-5.4 scores higher for coding on the public lane, 47.7 to 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-5.4 or Llama 4 Scout?
GPT-5.4 scores higher for agentic tasks on the public lane, 52.1 to 6.8. 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-5.4 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-5.4 or Llama 4 Scout?
Llama 4 Scout has the larger documented context window: 10M, compared with 1.05M.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5.4
API / mo$13,125
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Llama 4 Scout
API / mo$0
Self-host / mo$3,495
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 evidence42 rows
Agentic
Terminal-Bench 2.0
GPT-5.475.1%SourceLlama 4 Scout—Not directly comparable
CyberGym
GPT-5.479.0%SourceLlama 4 Scout—Not directly comparable
BrowseComp
GPT-5.482.7%SourceLlama 4 Scout—Not directly comparable
OSWorld-Verified
GPT-5.475%SourceLlama 4 Scout—Not directly comparable
MCP Atlas
GPT-5.470.6%SourceLlama 4 Scout—Not directly comparable
Toolathlon
GPT-5.454.6%SourceLlama 4 Scout—Not directly comparable
τ²-bench results
GPT-5.498.9%SourceLlama 4 Scout—Not directly comparable
Claw-Eval
GPT-5.460.3%SourceLlama 4 Scout—Not directly comparable
DeepSearchQA
GPT-5.473.6%SourceLlama 4 Scout—Not directly comparable
Gert Labs
GPT-5.464.89%SourceLlama 4 Scout—Not directly comparable
ResearchClawBench
GPT-5.415.3%SourceLlama 4 Scout—Not directly comparable
JobBench
GPT-5.438.9%SourceLlama 4 Scout—Not directly comparable
ExploitGym
GPT-5.46.0%SourceLlama 4 Scout—Not directly comparable
ApprenticeBench
GPT-5.411%SourceLlama 4 Scout—Not directly comparable
MazeBench
GPT-5.40%SourceLlama 4 Scout—Not directly comparable
Coding
LiveCodeBench Pro
GPT-5.487.5%SourceLlama 4 Scout—Not directly comparable
SWE-bench Pro
GPT-5.457.7%SourceLlama 4 Scout—Not directly comparable
React Native Evals
GPT-5.485.3%SourceLlama 4 Scout—Not directly comparable
Terminal-Bench 2.0
GPT-5.475.1%SourceLlama 4 Scout—Not directly comparable
Vibe Code Bench
GPT-5.467.42%SourceLlama 4 Scout—Not directly comparable
PostTrainBench v1.1
GPT-5.419.0%SourceLlama 4 Scout—Not directly comparable
Reasoning
Multimodal
MMMU-Pro
GPT-5.481.2%SourceLlama 4 Scout—Not directly comparable
OfficeQA Pro
GPT-5.453.2%SourceLlama 4 Scout—Not directly comparable
MMMU-Pro w/ Python
GPT-5.482.1%SourceLlama 4 Scout—Not directly comparable
CharXiv
GPT-5.482.8%SourceLlama 4 Scout—Not directly comparable
ERQA
GPT-5.465.4%SourceLlama 4 Scout—Not directly comparable
SimpleVQA
GPT-5.461.1%SourceLlama 4 Scout—Not directly comparable
ScreenSpot Pro
GPT-5.485.4%SourceLlama 4 Scout—Not directly comparable
ZeroBench
GPT-5.441.0%SourceLlama 4 Scout—Not directly comparable
MedXpertQA (MM)
GPT-5.477.1%SourceLlama 4 Scout—Not directly comparable
Knowledge
GPQA
GPT-5.492.8%SourceLlama 4 Scout—Not directly comparable
HLE
GPT-5.452.1%SourceLlama 4 Scout—Not directly comparable
HLE w/o tools
GPT-5.439.8%SourceLlama 4 Scout—Not directly comparable
GPQA-D
GPT-5.492.8%SourceLlama 4 Scout—Not directly comparable
HealthBench Hard
GPT-5.440.1%SourceLlama 4 Scout—Not directly comparable
MedXpertQA (Text)
GPT-5.459.6%SourceLlama 4 Scout—Not directly comparable
HealthBench Professional
GPT-5.448.1%SourceLlama 4 Scout—Not directly comparable
Math
42 public results · 0 shared
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Last updated October 17, 2026