Math
Like-for-like- DeepSeek V3
- 1.7
- Llama 4 Scout
- Not measured
- Weighted basis
- 1 vs 1 rows
- Reading
- No winner stated
Model comparison
DeepSeek V3 vs Llama 4 Scout
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
DeepSeek V3 has the higher public score estimate, 44.15 versus 39.08, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3 does not fit this workload in one request. Llama 4 Scout has no comparable published API token rate.
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.
5 DeepSeek V3 only
1 Shared
- Shared results
- 1
- DeepSeek V3 only
- 5
- Llama 4 Scout only
- 0
- Like-for-like categories
- 1 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Agentic
Not comparable- DeepSeek V3
- Not measured
- Llama 4 Scout
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- DeepSeek V3
- 38.9
- Llama 4 Scout
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3
- Not measured
- Llama 4 Scout
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3
- 72.7
- Llama 4 Scout
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3
- Not measured
- Llama 4 Scout
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3
- Not measured
- Llama 4 Scout
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3
- 86.1
- Llama 4 Scout
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | DeepSeek V3 | Llama 4 Scout | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 1.7 | Not measured | Like-for-like1 vs 1 rows | No winner stated |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | 38.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 72.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | 86.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
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
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
- DeepSeek V3
- $0.00082
- 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
- DeepSeek V3
- $0.0168
- 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
- DeepSeek V3
- $0.0304
- Does not fit in one request
- Llama 4 Scout
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
DeepSeek V3 does not fit this workload in one request. 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.
SpecificationDeepSeek V3Llama 4 Scout
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3
128K
Llama 4 Scout
10M
API model ID
DeepSeek V3
Not sourced
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.
DeepSeek V3
$0.07 per 1M cached input tokens
Llama 4 Scout
No comparable hosted API rate
Documented inputs
DeepSeek V3
Not sourced
Llama 4 Scout
Not sourced
Documented outputs
DeepSeek V3
Not sourced
Llama 4 Scout
Not sourced
Provider availability
DeepSeek V3
Not sourced
Llama 4 Scout
Not sourced
Reasoning profile
DeepSeek V3
Non-Reasoning
Llama 4 Scout
Non-Reasoning
Weight access
DeepSeek V3
Open Weight
Llama 4 Scout
Open Weight
License
DeepSeek V3
Open Weight
Llama 4 Scout
Open Weight
Release date
DeepSeek V3
2024-12-26
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
- DeepSeek V3 has the higher public score estimate, 44.15 versus 39.08, but the 90% score intervals 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.
DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
Llama 4 Scout
API / mo$0
Self-host / mo$2,278
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 evidence6 rows
Coding
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceDeepSeek V31.724%Llama 4 Scout0.000%DeepSeek V3 leads this result
Instruction following
IFEval
DeepSeek V386.1%SourceLlama 4 Scout—Not directly comparable
Frequently asked questions
Which is better, DeepSeek V3 or Llama 4 Scout?
DeepSeek V3 has the higher public score estimate, 44.15 versus 39.08, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, DeepSeek V3 or Llama 4 Scout?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, DeepSeek V3 or Llama 4 Scout?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, DeepSeek V3 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, DeepSeek V3 or Llama 4 Scout?
Llama 4 Scout has the larger documented context window: 10M, compared with 128K.
Related comparisons
Last updated July 30, 2026
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