DeepSeek-R1 vs Llama 4 Maverick
DeepSeek vs Meta · Prices checked November 11, 2026
Llama 4 Maverick is 48 per cent cheaper on blended cost, $0.5450 per million tokens against $1.04. A single 100K-input, 10K-output job costs $0.0450 on Llama 4 Maverick against $0.0769 on DeepSeek-R1.
Pricing and capacity
RatesUSD per million tokens. Prices checked November 11, 2026.
| Metric | DeepSeek-R1 | Llama 4 Maverick | Difference |
|---|---|---|---|
| Input / 1M tokens | $0.5500 | $0.3500 | 36% |
| Output / 1M tokens | $2.19 | $1.00 | 2.2× |
| Blended · 70/30calculated | $1.04 | $0.5450 | 48% |
| Blended · 50/50calculated | $1.37 | $0.6750 | 2.0× |
| Blended · 80/20calculated | $0.8780 | $0.4800 | 45% |
| Blended · 20/80calculated | $1.86 | $0.8700 | 2.1× |
| Job cost · 100K + 10Kcalculated | $0.0769 | $0.0450 | 41% |
| Job cost · 1M + 100Kcalculated | $0.7690 | $0.4500 | 41% |
| Context window | 64,000 | 128,000 | 2.0× |
Input and output rates are as published by each provider. Blended and job-cost rows are calculated comparison metrics, not published prices — how they are derived.
DeepSeek-R1 leads on 6 of the 6 benchmarks both models report. The widest gap is on LMArena Elo, where DeepSeek-R1 leads by 71 points. Llama 4 Maverick takes a 128,000-token context window against 64,000, giving 2× more room for long documents or whole-codebase work.
Benchmarks
| Benchmark | DeepSeek-R1 | Llama 4 Maverick | Leader |
|---|---|---|---|
| MMLU | 90.8% | 85.5% | DeepSeek-R1 |
| GPQA Diamond | 71.5% | 69.8% | DeepSeek-R1 |
| MATH | 95% | 61.2% | DeepSeek-R1 |
| HumanEval | 90% | 88% | DeepSeek-R1 |
| SWE-bench Verified | 49.2% | 36% | DeepSeek-R1 |
| LMArena Elo | 1398 Elo | 1327 Elo | DeepSeek-R1 |
Only suites both models report are listed, so a missing score is never shown as a loss. Figures are provider-published or from public leaderboards and are not independently re-run.
What each model is for
DeepSeek-R1
DeepSeek · Reasoning
Open-weights reasoning model with chain-of-thought trained via RL. Comparable to o1 on math benchmarks at a fraction of the price.
- Open-weights reasoning
- Visible chain-of-thought
- Math & code
- Cheap
Llama 4 Maverick
Meta · Frontier
The flagship Llama 4, an MoE-architecture model designed for cheap, high-throughput inference across the open-weights ecosystem.
- MoE for cheap inference
- Open weights
- Wide hosted availability
- Multimodal
Frequently asked questions
Which is cheaper, DeepSeek-R1 or Llama 4 Maverick?
Llama 4 Maverick is 48 per cent cheaper on blended cost, $0.5450 per million tokens against $1.04. A single 100K-input, 10K-output job costs $0.0450 on Llama 4 Maverick against $0.0769 on DeepSeek-R1. Blended cost weights input at 70% and output at 30%; price your own token split for an exact answer, since the cheaper model on a blended basis is not always cheaper for your workload.
Which performs better, DeepSeek-R1 or Llama 4 Maverick?
DeepSeek-R1 leads on 6 of the 6 benchmarks both models report. The widest gap is on LMArena Elo, where DeepSeek-R1 leads by 71 points. These are the figures each provider or public leaderboard publishes, not results we re-run, and a benchmark average can hide the specific behaviour your workload depends on.
Which has the larger context window, DeepSeek-R1 or Llama 4 Maverick?
Llama 4 Maverick takes a 128,000-token context window against 64,000, giving 2× more room for long documents or whole-codebase work. The context window caps everything in a single request: prompt, retrieved documents, conversation history and the model’s own output all share it.
Can I compare DeepSeek-R1 and Llama 4 Maverick on my own numbers?
Yes. Open both in the side-by-side comparison tool to see every field at once, or use the token calculator to price your actual prompt and monthly request volume against each model. Both are free and need no sign-up.
Price it for your own workload
Related matchups
Terms used on this page
Prices and scores are recomputed on every deploy from the catalogue. Prices checked November 11, 2026.