Leaderboard · Model · Updated
Llama 3.3 70B Instruct
Meta's 70B open-weights model — frontier-class for its size, runs on prosumer GPUs.
At a glance
| Provider | Meta |
|---|---|
| Released | 2024-12 |
| Tier | open-weights |
| License | Open-weights (Llama 3.3 Community License) |
| Modalities | text |
| Context window | 128k tokens |
| Max output | 4.096k tokens |
| API price · input | $0.23 / 1M tokens |
| API price · output | $0.40 / 1M tokens |
| Hugging Face | meta-llama/Llama-3.3-70B-Instruct |
Benchmark performance
How Llama 3.3 70B Instruct stacks up against the current leader (GPT-5) and the median model in the leaderboard:
| Benchmark | Llama 3.3 70B Instruct | GPT-5 | Median |
|---|---|---|---|
| Chatbot Arena Elo | 1257 | 1410 | 1320 |
| MMLU-Pro | 68.9 | 86.8 | 78.0 |
| GPQA Diamond | 50.5 | 87.3 | 65.0 |
| MATH | 77.0 | 96.7 | 78.3 |
| HumanEval | 88.4 | 95.1 | 92.0 |
| SWE-Bench Verified | N/A | 74.9 | 49.0 |
Numbers compiled from provider technical reports and Chatbot Arena. See methodology for the composite-score formula.
OpenRouter routes your requests across Llama 3.3 70B Instruct, GPT-5, Claude, Gemini, and 100+ other models behind a single API key — pay-as-you-go, no monthly minimum. Try OpenRouter → (affiliate · supports this site)
What does Llama 3.3 70B Instruct cost in practice?
API pricing is $0.23 per 1M input tokens and $0.40 per 1M output tokens. Assuming a 50/50 input/output split, here is what that looks like at three workload sizes:
| Volume | Per day | Per month | Per year |
|---|---|---|---|
| 1M tokens/day | $0.32 | $9.45 | $114.97 |
| 10M tokens/day | $3.15 | $94.50 | $1,150 |
| 100M tokens/day | $31.50 | $945.00 | $11,498 |
Strengths & weaknesses
Where it shines
No clear top-tier strengths — this is a mid-pack model.
Where it lags
- Arena: 1257 (rank #23 of 27, below average)
- MMLU-Pro: 68.9 (rank #24 of 29, below average)
- GPQA: 50.5 (rank #22 of 29, mid-pack)
Best alternatives
The closest models to Llama 3.3 70B Instruct by tier and benchmark score:
| Model | Score | $ in / out | Context | Action |
|---|---|---|---|---|
| Qwen2.5 72B Instruct Alibaba |
65.6 | $0.35 / $0.40 | 131.072k | Try → · vs Llama |
| Llama 3.1 405B Instruct Meta |
65.7 | $2.70 / $2.70 | 128k | Try → · vs Llama |
| DeepSeek V3 DeepSeek |
68.0 | $0.27 / $1.10 | 128k | Try → · vs Llama |
| Qwen2.5-Coder 32B Alibaba |
68.8 | $0.18 / $0.18 | 131.072k | Try → · vs Llama |
| Llama 3.1 70B Instruct Meta |
60.2 | $0.23 / $0.40 | 128k | Try → · vs Llama |
Frequently asked questions
Is Llama 3.3 70B Instruct a good model?
Llama 3.3 70B Instruct scores 64.7 on the llmrank.top composite (rank #24 of 30). It trails the frontier — for top performance, look at GPT-5 (86.0). Whether it's the right fit depends on your workload — see the use-case discussion on this page.
How much does Llama 3.3 70B Instruct cost?
Llama 3.3 70B Instruct is priced at $0.23 per 1M input tokens and $0.40 per 1M output tokens (USD). For a 10M-token-per-day workload split 50/50, that works out to roughly $3.15/day or $1,150/year.
What is Llama 3.3 70B Instruct's context window?
128k tokens. For very long inputs, consider a 1M+ context model like Gemini 2.5 Pro or GPT-4.1.
Is Llama 3.3 70B Instruct open source?
Yes — released under the Llama 3.3 Community License license, weights are downloadable from the provider or Hugging Face.
What is Llama 3.3 70B Instruct's SWE-Bench score?
Llama 3.3 70B Instruct scores not reported on SWE-Bench Verified — the benchmark that measures real-world GitHub issue resolution. Its Chatbot Arena Elo is 1257.
What are the best alternatives to Llama 3.3 70B Instruct?
Closest alternatives by tier and score: Qwen2.5 72B Instruct, Llama 3.1 405B Instruct, DeepSeek V3. See the alternatives section on this page for side-by-side numbers.
Related: Qwen2.5 72B Instruct · Llama 3.1 405B Instruct · DeepSeek V3 · Full leaderboard
Spotted out-of-date numbers? Open an issue — corrections usually ship within 24h.
Try Llama 3.3 70B Instruct now
Direct link to the official playground — or use OpenRouter to A/B test it against any other model on a single API key.