Model comparison
DeepSeek V3.2 vs Llama 4 Maverick
Head-to-head evidence from 13 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); Llama 4 Maverick #191 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and Llama 4 Maverick share 13 comparable benchmark results. 1 of 8 categories are comparable. 6 results are unique to DeepSeek V3.2; 5 to Llama 4 Maverick.
Updated July 20, 2026- Shared results
- 13
- DeepSeek V3.2 only
- 6
- Llama 4 Maverick only
- 5
- Comparable categories
- 1 / 8
Pick DeepSeek V3.2 if you want the stronger benchmark profile. Llama 4 Maverick only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 7 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 V3.2 is clearly ahead on the BenchAlign aggregate, 55.4 to 23.49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2's sharpest advantage is in mathematics, where it averages 17.1 against 0.7. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 22.100% to 0.690%.
DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 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. Llama 4 Maverick gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.
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 V3.2 | Δ | Llama 4 Maverick |
|---|---|---|---|
| Math | DeepSeek V3.217.1 | Margin← 16.4 | Llama 4 Maverick0.7 |
| Coding | DeepSeek V3.260.9 | MarginNo overlap | Llama 4 MaverickNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 22.100%B 0.690%Winner: DeepSeek V3.2Δ 21.4FrontierMath v2 (Tiers 1-3): DeepSeek V3.2 scored 22.100%; Llama 4 Maverick scored 0.690%. DeepSeek V3.2 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Llama 4 Maverick | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Llama 4 Maverick$0 input / $0 output | Llama 4 Maverick has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Llama 4 Maverick121 tok/s | Llama 4 Maverick has the higher measured throughput. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Llama 4 Maverick0.95 s | Llama 4 Maverick reaches the first token sooner. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Llama 4 Maverick1M | Llama 4 Maverick lists the larger context window. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | DeepSeek V3.2 | Llama 4 Maverick | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 17.8% | DeepSeek V3.2 leads |
| Gert LabsSource | 29.57% | — | Not comparable |
| AA Agentic IndexSource | — | 1.3% | Not comparable |
| GDPval-AASource | — | 0.0% | Not comparable |
| GDPval-AASource | — | -16 | Not comparable |
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek V3.2 | Llama 4 Maverick | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 14.3% | DeepSeek V3.2 leads |
| AA-GPQA DiamondSource | 75.1% | 67.1% | DeepSeek V3.2 leads |
| AA-HLESource | 10.5% | 4.8% | DeepSeek V3.2 leads |
| AA-Omniscience IndexSource | -46.7% | -41.8% | Llama 4 Maverick leads |
| AA-Omniscience AccuracySource | 24.2% | 24.3% | Llama 4 Maverick leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 87.3% | Llama 4 Maverick leads |
MathDeepSeek V3.2 wins2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Llama 4 Maverick | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 43.0% | DeepSeek V3.2 leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V3.2 or Llama 4 Maverick?
DeepSeek V3.2 is ahead on BenchLM's BenchAlign leaderboard, 55.4 to 23.49. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 22.100% and 0.690%.
Which is better for math, DeepSeek V3.2 or Llama 4 Maverick?
DeepSeek V3.2 has the edge for math in this comparison, averaging 17.1 versus 0.7. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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