Llama 3.3 70B Instruct (free)
meta/llama-3-3-70b-instruct-free出品方： Meta · 系列: llama · 发布 2024-12-06 · 知识截止: 2024-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding63
- Tool calling40/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability1/10
Agents76
- Tool calling35/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output80
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume0/20
Cost efficiency0
- Has published price0/100
Long context36
- Context window (100K → 2M)36/90
- Has published price for full window0/10
Production-readiness45
- Number of independent providers5/40
- Has published per-token price0/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
This model has no published per-token price, so we can't compute a cost estimate. See the provider's official pricing page for current rates.
在 1 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| OpenRouter openrouter | meta-llama/llama-3.3-70b-instruct:free | Unknown | Unknown | 131K | 2024-12-06 |
Frequently asked questions
How much does Llama 3.3 70B Instruct (free) cost?
Llama 3.3 70B Instruct (free) does not have a publicly published per-token price in our data source. This usually means it is gated behind enterprise sales or invite access. Check Meta's official pricing page for the most current rates.
What is the context window of Llama 3.3 70B Instruct (free)?
Llama 3.3 70B Instruct (free) has a context window of 131K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.3 70B Instruct (free) support tool calling?
Yes. Llama 3.3 70B Instruct (free) supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Llama 3.3 70B Instruct (free) support structured output / JSON mode?
Yes. Llama 3.3 70B Instruct (free) supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Llama 3.3 70B Instruct (free) accept image input?
No. Llama 3.3 70B Instruct (free) only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama 3.3 70B Instruct (free) open-weight?
Yes. Llama 3.3 70B Instruct (free)'s weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Llama 3.3 70B Instruct (free)?
If Llama 3.3 70B Instruct (free) doesn't fit, consider Meta-Llama-3.1-8B-Instruct, Llama-3.3-70B-Instruct, Llama 4 Maverick 17B 128E Instruct FP8. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Meta models
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- Llama-3.3-70B-Instruct$0.05 in / $0.23 out
- Llama 4 Maverick 17B 128E Instruct FP8$0.14 in / $0.59 out
- Llama 4 Scout 17B 16E Instruct$0.08 in / $0.30 out
- Meta-Llama-3.1-70B-Instruct$0.40 in / $0.40 out
Capability lists this model is in
最近更新：
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.