DeepSeek-V3.2 Base vs Llama 4 Maverick Chat: which model should you choose?
We compared DeepSeek-V3.2 Base and Llama 4 Maverick Chat on price, context window and 12 benchmarks. DeepSeek-V3.2 Base has the stronger average benchmark score; Llama 4 Maverick Chat is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Base
DeepSeek-V3.2 Base is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.05 per million input tokens and $27.10 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $27.10 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
Llama 4 Maverick Chat
Llama 4 Maverick Chat is a multimodal model from Meta AI. It offers a 128,000-token context window, costs $5.09 per million input tokens and $23.20 per million output tokens, and averages 79.7 across the index's benchmark suite.
- Input
- $5.09 / 1M
- Output
- $23.20 / 1M
- Context
- 128,000
- Avg score
- 79.7
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-V3.2 Base | Llama 4 Maverick Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.09 | A |
| Output price / 1M | $27.10 | $23.20 | B |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 128,000 | 32,768 | A |
| Average score | 80.2 | 79.7 | A |
| Arena Elo | 1417.9 | 1419.9 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-V3.2 Base wins 6, Llama 4 Maverick Chat wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Base | Llama 4 Maverick Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 80.5 | 3.8 | DeepSeek-V3.2 Base |
| GPQA Diamond | 77.1 | 73.0 | 4.1 | DeepSeek-V3.2 Base |
| SWE-bench Verified | 69.1 | 66.6 | 2.5 | DeepSeek-V3.2 Base |
| HumanEval | 82.8 | 83.2 | 0.4 | Llama 4 Maverick Chat |
| MATH-500 | 83.7 | 84.8 | 1.1 | Llama 4 Maverick Chat |
| AIME 2025 | 74.3 | 78.9 | 4.6 | Llama 4 Maverick Chat |
| LiveCodeBench | 73.1 | 76.3 | 3.2 | Llama 4 Maverick Chat |
| IFEval | 86.9 | 87.4 | 0.5 | Llama 4 Maverick Chat |
| TruthfulQA | 83.5 | 81.5 | 2.0 | DeepSeek-V3.2 Base |
| HellaSwag | 90.2 | 88.8 | 1.4 | DeepSeek-V3.2 Base |
| MMMU | 76.7 | 75.4 | 1.3 | DeepSeek-V3.2 Base |
| Arena Elo | 1417.9 | 1419.9 | 2.0 | Llama 4 Maverick Chat |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Base costs about $10.56 per million tokens and Llama 4 Maverick Chat costs about $9.62. Llama 4 Maverick Chat is roughly 9% cheaper than DeepSeek-V3.2 Base. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Base wins 6, Llama 4 Maverick Chat wins 6, and 0 are tied. On the index average, DeepSeek-V3.2 Base leads Llama 4 Maverick Chat by 0.5 points (80.2 versus 79.7).
The price picture favours Llama 4 Maverick Chat. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with DeepSeek-V3.2 Base for quality-sensitive work and Llama 4 Maverick Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 Base if
- You want the higher average benchmark score in this index.
- You need the larger 128,000-token output limit.
Choose Llama 4 Maverick Chat if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-V3.2 Base better than Llama 4 Maverick Chat?
On the index average, DeepSeek-V3.2 Base leads Llama 4 Maverick Chat by 0.5 points (80.2 versus 79.7). The better choice depends on price, context and your task. DeepSeek-V3.2 Base wins 6 benchmarks, Llama 4 Maverick Chat wins 6.
Which is cheaper, DeepSeek-V3.2 Base or Llama 4 Maverick Chat?
Llama 4 Maverick Chat is cheaper on a blended 3:1 basis at about $9.62 per million tokens, versus $10.56 for DeepSeek-V3.2 Base.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
DeepSeek-V3.2 Base is open weights; Llama 4 Maverick Chat is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 Base for quality-sensitive work and Llama 4 Maverick Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.