Llama 3.1 405B vs Yi-Large
Meta vs 01.AI · Prices checked 12 August 2026
Llama 3.1 405B is the cheaper of the two by a wide margin: $0.800 per million tokens blended against $3.00, roughly 3.7× less. A single 100K-input, 10K-output job costs $0.088 on Llama 3.1 405B against $0.330 on Yi-Large.
Llama 3.1 405B leads on 4 of the 4 benchmarks both models report. The widest gap is on LMArena Elo, where Llama 3.1 405B leads by 91 points. Llama 3.1 405B takes a 128,000-token context window against 32,000, giving 4× more room for long documents or whole-codebase work.
Pricing and capacity
| Metric | Llama 3.1 405B | Yi-Large | Difference |
|---|---|---|---|
| Input / 1M tokens | $0.800 | $3.00 | 3.8× |
| Output / 1M tokens | $0.800 | $3.00 | 3.8× |
| Blended · 70/30calculated | $0.800 | $3.00 | 3.7× |
| Blended · 50/50calculated | $0.800 | $3.00 | 3.8× |
| Blended · 80/20calculated | $0.800 | $3.00 | 3.8× |
| Blended · 20/80calculated | $0.800 | $3.00 | 3.8× |
| Job cost · 100K + 10Kcalculated | $0.088 | $0.330 | 3.8× |
| Job cost · 1M + 100Kcalculated | $0.880 | $3.30 | 3.7× |
| Context window | 128,000 | 32,000 | 4.0× |
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.
Benchmarks
| Benchmark | Llama 3.1 405B | Yi-Large | Leader |
|---|---|---|---|
| MMLU | 88% | 78% | Llama 3.1 405B |
| MATH | 73% | 60% | Llama 3.1 405B |
| HumanEval | 89% | 70% | Llama 3.1 405B |
| LMArena Elo | 1290 Elo | 1199 Elo | Llama 3.1 405B |
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
Llama 3.1 405B
Meta · Large
Largest dense Llama. Used as a quality benchmark for open weights, heavy to run, often hosted via Together / DeepInfra / Cerebras.
- Top open-weights quality
- Permissive license
- Strong reasoning
Frequently asked questions
Which is cheaper, Llama 3.1 405B or Yi-Large?
Llama 3.1 405B is the cheaper of the two by a wide margin: $0.800 per million tokens blended against $3.00, roughly 3.7× less. A single 100K-input, 10K-output job costs $0.088 on Llama 3.1 405B against $0.330 on Yi-Large. 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, Llama 3.1 405B or Yi-Large?
Llama 3.1 405B leads on 4 of the 4 benchmarks both models report. The widest gap is on LMArena Elo, where Llama 3.1 405B leads by 91 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, Llama 3.1 405B or Yi-Large?
Llama 3.1 405B takes a 128,000-token context window against 32,000, giving 4× 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 Llama 3.1 405B and Yi-Large 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 12 August 2026.