Phi-4
Input price$0.07
Output price$0.14
MicrosoftText
Llama-3.3-70B-Instruct
Llama-3.3-70B-Instruct is Microsoft's model at $0.10 input and $0.32 output per million tokens, with a 128K context window. On a typical 3:1 input-to-output mix that blends to roughly $0.16 per million tokens, placing it in the budget band of the 68 models tracked here — cheaper than 84% of them.
Context window128K
Input price$0.1 / 1M
Output price$0.32 / 1M
Output speed100 tok/s
Quality score78
Data updatedUpdated Jun 2026
Price verified2026-08-04 · OpenRouter
By price, Llama-3.3-70B-Instruct ranks 7th cheapest of 68 on input and 14th on output. Its quality score of 78 ranks 56th of 68, and on value (quality score ÷ output price) it ranks 14th of 68. Its blended rate sits 95% below the catalog average.
In real billing terms: a support chatbot handling 50,000 turns a month (2,000 input / 500 output tokens each) costs about $18 per month on Llama-3.3-70B-Instruct, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $21. Because output is priced 3.2× input here, long-generation tasks scale cost fastest — cap output length before you optimise anything else.
A 128K context window holds roughly 171 pages of A4 text (at ~750 tokens per page), ranking 53rd of 68 here. That covers typical RAG and multi-turn chat, though book-length inputs still need retrieval and chunking. Output runs at about 100 tok/s (30th fastest of 68), acceptable interactively, though long responses will feel slow.
We track 4 Microsoft models, and Llama-3.3-70B-Instruct's blended rate sits 40% above that provider's average. If your task tolerates a little less headroom, qwen3.5-flash (Alibaba) blends about 65% cheaper at a quality score of 82, and is worth benchmarking first. Going the other way, Hunyuan HY3 scores 87 on quality for about 36% less.
The closest model in Microsoft's line-up is MAI-DS-R1 ($0.14 input / $0.28 output, quality 90). Against it, Llama-3.3-70B-Instruct scores 12 points lower on quality, runs 40 tok/s faster (about 67%), costs 13% less blended. When two models sit this close on price, the deciding factor is usually the quality ceiling — run both against your real prompts before picking.
Estimated monthly cost
Monthly API spend across four common workloads, next to the cheapest model we track.
| Workload | Usage assumption | This model | Cheapest model |
|---|---|---|---|
| Support chatbot | 2,000 input / 500 output tokens per turn, 50,000 turns per month | $18 | $3.00 |
| RAG document Q&A | 8,000 input / 800 output tokens per query, 20,000 queries per month | $21 | $3.84 |
| Coding assistant | 12,000 input / 2,000 output tokens per request, 10,000 requests per month | $18 | $3.20 |
| Batch summarization | 4,000 input / 400 output tokens per item, 200,000 items per month | $106 | $19 |
Derived directly from the rates on this page. Excludes prompt caching, batch discounts and free tiers.
How it compares
The alternatives worth benchmarking alongside it, from the same dataset.
| Model | Provider | Blended /1M | Quality | Price delta |
|---|---|---|---|---|
| Llama-3.3-70B-Instruct | Microsoft | $0.16 | 78 | — |
| qwen3.5-flashCheaper option | Alibaba | $0.055 | 82 | -65% |
| Hunyuan HY3Higher quality | Tencent | $0.10 | 87 | -36% |
| MAI-DS-R1Same provider | Microsoft | $0.18 | 90 | +13% |
| Phi-4-miniBest value overall | Microsoft | $0.025 | 68 | -84% |
Best for
ChatbotCoding
Modalities
Text
Where it fits — and where it doesn't
Good fit
- Coding: writing, refactoring and debugging across multi-file project context.
- Chat and support: natural multi-turn conversation for user-facing products.
- High-volume, cost-sensitive batch work: low rates suit classification, tagging and summarization at scale.
Poor fit
- Hard reasoning: a quality score of 78 is on the low side here; pick a stronger model for complex planning or maths.
- Image understanding: this model is text-only, so vision tasks need a multimodal alternative.
Frequently asked questions
How much does the Llama-3.3-70B-Instruct API cost?
Llama-3.3-70B-Instruct costs $0.10 per million input tokens and $0.32 per million output tokens. On a 3:1 input-to-output mix that blends to about $0.16 per million tokens.
What does Llama-3.3-70B-Instruct cost per month?
It depends on volume. Using the four worked scenarios on this page: a support chatbot runs about $18/month, RAG document Q&A about $21/month, a coding assistant about $18/month, and batch summarization about $106/month. Plug your own token counts into the cost calculator for a tailored figure.
How large is the Llama-3.3-70B-Instruct context window?
128K tokens — roughly 171 pages of A4 text at ~750 tokens per page, ranking 53rd of the 68 models tracked here.
Llama-3.3-70B-Instruct or qwen3.5-flash — which is better value?
qwen3.5-flash blends to about $0.055 per million tokens versus $0.16 for Llama-3.3-70B-Instruct — roughly 65% cheaper — at a quality score of 82 against 78. If that quality gap does not show up on your task, take the cheaper one; if it does, stay with Llama-3.3-70B-Instruct. Benchmark both on your own data before deciding.
What is Llama-3.3-70B-Instruct best suited for?
Tagged use cases: chatbot, coding. Coding: writing, refactoring and debugging across multi-file project context. Less suitable: Hard reasoning: a quality score of 78 is on the low side here; pick a stronger model for complex planning or maths.
How current is this Llama-3.3-70B-Instruct pricing?
Pricing here was last verified on 2026-08-04 against OpenRouter. llmprice.app runs a daily collection job, but providers can change rates between runs — confirm against Microsoft's official pricing page before you commit.
Estimate the real cost of this model
Bring tokens per request and monthly volume into the calculator to compare total model costs.
Related models
Continue by provider and use case.
View model
Phi-4-mini
Input price$0.02
Output price$0.04
MAI-DS-R1
Input price$0.14
Output price$0.28
Pricing, speed and quality scores change over time. Confirm official documentation before production use.