Llama 4 Maverick vs DeepSeek R1 (Full): Which is Better in 2026?
Llama 4 Maverick (Meta, 400B parameters) and DeepSeek R1 (Full) (DeepSeek, 671B parameters) are both frontier-class models competing for the same developer and enterprise audience in 2026. This page compiles benchmarks, task grades, and practical guidance to help you decide which model fits your workflow.
Last updated: October 2, 2026
Quick Verdict
Across the benchmarks where both models have published scores, DeepSeek R1 (Full) leads on 3 of 3 shared evaluation tasks. Llama 4 Maverick remains competitive, particularly in areas aligned with its training focus. For general-purpose quality, DeepSeek R1 (Full) currently holds an edge — but the right choice depends heavily on your specific use case, budget, and whether you need API access or self-hosted deployment.
Side-by-Side Comparison
The table below covers every benchmark for which at least one model has a published score. Higher scores are better on all metrics except where noted. Bold values indicate the higher score in each row.
| Benchmark | Llama 4 Maverick | DeepSeek R1 (Full) | Winner |
|---|---|---|---|
| MMLU-Pro | 80.5 | 84 | DeepSeek R1 (Full) |
| GPQA Diamond | 69.8 | 71.5 | DeepSeek R1 (Full) |
| HumanEval | 62 | — | Llama 4 Maverick |
| LiveCodeBench | 43.4 | 65.9 | DeepSeek R1 (Full) |
| Arena Elo (Text) | 1287 | — | Llama 4 Maverick |
| MMMU | 73.4 | — | Llama 4 Maverick |
| Arena Elo (Vision) | 1141 | — | Llama 4 Maverick |
| Humanity's Last Exam | 5.7 | — | Llama 4 Maverick |
| Aider Polyglot | 15.6 | — | Llama 4 Maverick |
| MATH-500 | — | 97.3 | DeepSeek R1 (Full) |
| IFEval | — | 83.3 | DeepSeek R1 (Full) |
Task Performance
Per-task grades are sourced from the id8 LLM leaderboard evaluations. Each grade reflects observed output quality across real-world prompts in that category. A dash (—) means grades are not yet published for that model.
| Task | Llama 4 Maverick | DeepSeek R1 (Full) |
|---|---|---|
| Coding | C | B |
| Math | B | A |
| Content Writing | B | B |
| Reasoning | B | A |
| Studying | B | A |
| Chat / Conversation | B | B |
| Summarization | B | B |
| Agents / Tool Use | C | B |
| Vision / Multimodal | B | — |
| Data Analysis | B | A |
| Hindi / Multilingual | C | C |
| Interview Prep | C | A |
| overall | B | A |
Grade scale: S = Exceptional A = Strong B = Good C = Fair D = Weak
Key Differences
- Both models offer open weights for self-hosting: Llama 4 Maverick from Meta (400B parameters) and DeepSeek R1 (Full) from DeepSeek (671B parameters).
- In terms of raw scale, DeepSeek R1 (Full) (671B parameters) is significantly larger than Llama 4 Maverick (400B parameters). Larger parameter counts often correlate with stronger reasoning, though efficiency improvements mean smaller models can punch above their weight.
- For graduate-level science and reasoning (GPQA Diamond), DeepSeek R1 (Full) leads with 71.5%, indicating stronger performance on expert-level knowledge tasks.
Which Should You Choose?
Choose Llama 4 Maverick if benchmark-verified reasoning and coding performance are your top priority. Its published scores on GPQA Diamond and SWE-Bench make it a strong choice for knowledge-intensive and software engineering workflows.
Choose DeepSeek R1 (Full) if your work centres on overall, Math, Reasoning, and self-hosting a 671B-parameter model is feasible for your infrastructure. It offers a strong value proposition for those use cases.
Still unsure? The LLM Leaderboard lets you sort and filter models by benchmark — useful for narrowing down the right model for a specific workload.
Explore Further
Use these tools to dig deeper into either model's hardware requirements and leaderboard ranking.
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