Model comparison
DeepSeek V4 Pro (High) vs Llama 4 Maverick
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro (High) #81 (Estimated); Llama 4 Maverick #191 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro (High) and Llama 4 Maverick share 16 comparable benchmark results. 1 of 8 categories are comparable. 22 results are unique to DeepSeek V4 Pro (High); 2 to Llama 4 Maverick.
Updated July 23, 2026- Shared results
- 16
- DeepSeek V4 Pro (High) only
- 22
- Llama 4 Maverick only
- 2
- Comparable categories
- 1 / 8
Pick DeepSeek V4 Pro (High) if you want the stronger benchmark profile. Llama 4 Maverick only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V4 Pro (High) is clearly ahead on the BenchAlign aggregate, 55.47 to 23.49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Pro (High)'s sharpest advantage is in mathematics, where it averages 94 against 0.7.
DeepSeek V4 Pro (High) is also the more expensive model on tokens at $0.43 input / $0.87 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 4 Maverick. That is roughly Infinityx on output cost alone. DeepSeek V4 Pro (High) is the reasoning model in the pair, while Llama 4 Maverick is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | DeepSeek V4 Pro (High) | Δ | Llama 4 Maverick |
|---|---|---|---|
| Math | DeepSeek V4 Pro (High)94.0 | Margin← 93.3 | Llama 4 Maverick0.7 |
| Agentic | DeepSeek V4 Pro (High)70.6 | MarginNo overlap | Llama 4 MaverickNot measured |
| Coding | DeepSeek V4 Pro (High)69.8 | MarginNo overlap | Llama 4 MaverickNot measured |
| Knowledge | DeepSeek V4 Pro (High)57.0 | MarginNo overlap | Llama 4 MaverickNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro (High) | Llama 4 Maverick | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro (High)$0.435 input / $0.87 output | Llama 4 Maverick$0 input / $0 output | Llama 4 Maverick has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Pro (High)Not available | Llama 4 Maverick121 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro (High)Not available | Llama 4 Maverick0.95 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro (High)1M | Llama 4 Maverick1M | Listed context windows are equal. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | DeepSeek V4 Pro (High) | Llama 4 Maverick | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.3% | — | Not comparable |
| BrowseCompSource | 80.4% | — | Not comparable |
| HLE w/ toolsSource | 44.7% | — | Not comparable |
| MCP AtlasSource | 74.2% | — | Not comparable |
| ToolathlonSource | 49% | — | Not comparable |
| τ²-bench resultsSource | 94.2% | 17.8% | DeepSeek V4 Pro (High) leads |
| GDPval-AASource | 39.9% | 0.0% | DeepSeek V4 Pro (High) leads |
| GDPval-AASource | 1299 | -16 | DeepSeek V4 Pro (High) leads |
| AA Agentic IndexSource | 34.4% | 1.3% | DeepSeek V4 Pro (High) leads |
Coding7 benchmarks
| Benchmark | DeepSeek V4 Pro (High) | Llama 4 Maverick | Result |
|---|---|---|---|
| CodeforcesSource | 2919.0 | — | Not comparable |
| SWE-bench VerifiedSource | 79.4% | — | Not comparable |
| SWE-bench ProSource | 54.4% | — | Not comparable |
| SWE MultilingualSource | 74.1% | — | Not comparable |
| Terminal-Bench 2.0Source | 63.3% | — | Not comparable |
| AA-SciCodeSource | 46.4% | 33.1% | DeepSeek V4 Pro (High) leads |
| AA Coding IndexSource | 58.7% | 16.3% | DeepSeek V4 Pro (High) leads |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | DeepSeek V4 Pro (High) | Llama 4 Maverick | Result |
|---|---|---|---|
| MMLU-ProSource | 87.1% | — | Not comparable |
| SimpleQASource | 46.2% | — | Not comparable |
| Chinese-SimpleQASource | 77.7% | — | Not comparable |
| GPQASource | 89.1% | — | Not comparable |
| GPQA-DSource | 89.1% | — | Not comparable |
| HLESource | 34.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 43.1% | 14.3% | DeepSeek V4 Pro (High) leads |
| AA-GPQA DiamondSource | 90.5% | 67.1% | DeepSeek V4 Pro (High) leads |
| AA-HLESource | 33.5% | 4.8% | DeepSeek V4 Pro (High) leads |
| AA-Omniscience IndexSource | -9.7% | -41.8% | DeepSeek V4 Pro (High) leads |
| AA-Omniscience AccuracySource | 41.8% | 24.3% | DeepSeek V4 Pro (High) leads |
| AA-Omniscience Hallucination RateSource | 88.6% | 87.3% | Llama 4 Maverick leads |
MathDeepSeek V4 Pro (High) wins5 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro (High) | Llama 4 Maverick | Result |
|---|---|---|---|
| AA-IFBenchSource | 71.3% | 43.0% | DeepSeek V4 Pro (High) leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V4 Pro (High) or Llama 4 Maverick?
DeepSeek V4 Pro (High) is ahead on BenchLM's BenchAlign leaderboard, 55.47 to 23.49.
Which is better for math, DeepSeek V4 Pro (High) or Llama 4 Maverick?
DeepSeek V4 Pro (High) has the edge for math in this comparison, averaging 94 versus 0.7. Llama 4 Maverick stays close enough that the answer can still flip depending on your workload.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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