Long documents
Prompts that approach the documented context limit
Llama 4 Maverick
Llama 4 Maverick has the larger documented context window.
Confidence: documented
DeepSeek V3 vs Llama 4 Maverick
Updated September 29, 2026. Rank says DeepSeek V3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
DeepSeek V3 has the higher public score estimate, 31.86 versus 22.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- DeepSeek V3 only
- 5
- Llama 4 Maverick only
- 0
- Like-for-like categories
- 0 / 8
Supported: DeepSeek V3 and Llama 4 MaverickHow 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
DeepSeek V3 and Llama 4 Maverick are 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
DeepSeek V3 and Llama 4 Maverick are 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
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 Maverick has no comparable published API token rate.
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.
18.6DeepSeek V318.1Llama 4 Maverick
Directional only · BenchAlign v5.7
DeepSeek V3 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
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.
5 DeepSeek V3 only
1 Shared
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.
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
Normalized gap 1.0DeepSeek V3:1.724%Llama 4 Maverick:0.690%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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- DeepSeek V3
- 12.1
- Estimated · #113/117
- Llama 4 Maverick
- 12.8
- Estimated · #112/117
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Directional only
Coding
Directional only- DeepSeek V3
- 18.6
- Estimated · #131/143
- Llama 4 Maverick
- 18.1
- Estimated · #135/143
- Basis
- BenchAlign v5.7 lane · 2 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- DeepSeek V3
- 30.2
- Estimated · #140/169
- Llama 4 Maverick
- 31.0
- Estimated · #137/169
- Basis
- BenchAlign v5.7 lane · 2 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- DeepSeek V3
- 38.2
- #103/124
- Llama 4 Maverick
- 48.9
- #82/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- DeepSeek V3
- 42.3
- Unranked · 2 rankable rows
- Llama 4 Maverick
- 56.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3
- Not ranked
- Llama 4 Maverick
- 52.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3
- Not ranked
- Llama 4 Maverick
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3
- 26.0
- Unranked · 1 rankable row
- Llama 4 Maverick
- 25.1
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V3 | Llama 4 Maverick | Basis | Reading |
|---|---|---|---|---|
| Agentic | 12.1Estimated · #113/117 | 12.8Estimated · #112/117 | Directional onlyBenchAlign v5.7 lane · 0 vs 0 public rows | Directional only |
| Coding | 18.6Estimated · #131/143 | 18.1Estimated · #135/143 | Directional onlyBenchAlign v5.7 lane · 2 vs 0 public rows | Directional only |
| Knowledge | 30.2Estimated · #140/169 | 31.0Estimated · #137/169 | Directional onlyBenchAlign v5.7 lane · 2 vs 0 public rows | Directional only |
| Instruction following | 38.2#103/124 | 48.9#82/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 42.3Unranked · 2 rankable rows | 56.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 52.6Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 26.0Unranked · 1 rankable row | 25.1Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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.
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
- DeepSeek V3
- $0.00082
- Fits in one request
- Llama 4 Maverick
- Self-hosted; infrastructure cost varies
- Fits in one request
Llama 4 Maverick 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 Maverick
- Self-hosted; infrastructure cost varies
- Fits in one request
Llama 4 Maverick 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 Maverick
- 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 Maverick 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.
SpecificationDeepSeek V3Llama 4 Maverick
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3
128K
Llama 4 Maverick
1M
API model ID
DeepSeek V3
Not sourced
Llama 4 Maverick
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 Maverick
No comparable hosted API rate
Documented inputs
DeepSeek V3
Not sourced
Llama 4 Maverick
Not sourced
Documented outputs
DeepSeek V3
Not sourced
Llama 4 Maverick
Not sourced
Provider availability
DeepSeek V3
Not sourced
Llama 4 Maverick
Not sourced
Reasoning profile
DeepSeek V3
Non-Reasoning
Llama 4 Maverick
Non-Reasoning
Weight access
DeepSeek V3
Open Weight
Llama 4 Maverick
Open Weight
License
DeepSeek V3
Open Weight
Llama 4 Maverick
Open Weight
Release date
DeepSeek V3
2024-12-26
Llama 4 Maverick
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, 31.86 versus 22.94, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Llama 4 Maverick has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, DeepSeek V3 or Llama 4 Maverick?
DeepSeek V3 has the higher public score estimate, 31.86 versus 22.94, 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 Maverick?
DeepSeek V3 scores higher for coding on the public lane, 18.6 to 18.1. DeepSeek V3 and Llama 4 Maverick are 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, DeepSeek V3 or Llama 4 Maverick?
Llama 4 Maverick scores higher for agentic tasks on the public lane, 12.8 to 12.1. DeepSeek V3 and Llama 4 Maverick are 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, DeepSeek V3 or Llama 4 Maverick?
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 Maverick?
Llama 4 Maverick has the larger documented context window: 1M, compared with 128K.
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 Maverick
API / mo$0
Self-host / mo$2,610
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
Instruction following
IFEval
DeepSeek V386.1%SourceLlama 4 Maverick—Not directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceDeepSeek V31.724%Llama 4 Maverick0.690%DeepSeek V3 leads this result
6 public results · 1 shared
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Last updated September 29, 2026