The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing.
75
Coding
68
Writing
72
Research
0
Images
91
Value
74
Long Context
Use this when
Teams needing capable open-weight LLM performance at budget pricing for coding assistance, summarization, or RAG pipelines.
Skip this if
You need state-of-the-art reasoning, nuanced creative writing, or multimodal (image) understanding — upgrade to Llama 3.1 405B or a frontier model instead.
Pricing
$0.40/1M in
$0.40/1M out
→0%since May 2026
Context
131k tokens
Speed
Fast
Pricing shown is via third-party API providers (e.g., OpenRouter, Together AI) — costs may vary. Meta releases Llama 3.1 weights publicly, enabling self-hosting at even lower cost. Not available directly from Meta as a hosted API.
Exceptional price-to-performance ratio at $0.40/1M tokens — far cheaper than GPT-4o or Claude Sonnet 4.6
Strong instruction-following and multilingual capabilities for its parameter count
131K context window supports document summarization and long RAG pipelines
Open-weight architecture allows self-hosting for data-sensitive workloads
Weaknesses
Noticeably behind Llama 3.1 405B and frontier models like GPT-5.4 on complex multi-step reasoning
Creative writing quality lacks the nuance and style control of Claude Sonnet 4.6
No native multimodal (image) support — text only
Real-world use cases
What people actually use Llama 3.1 70B Instruct for.
Building a cost-efficient RAG pipeline over internal documents using its 131K context window
Automating code review comments and docstring generation in CI/CD workflows
Summarizing lengthy research papers or legal documents at scale without high API costs
How Llama 3.1 70B Instruct compares
The nearest models people weigh against it, and what actually separates them.
vs Claude 3.5 Haiku — Against Claude 3.5 Haiku (Anthropic), Llama 3.1 70B Instruct runs about 83% cheaper per token, gives up 1.5x on context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs GPT-5 Mini — Against GPT-5 Mini (OpenAI), Llama 3.1 70B Instruct runs about 64% cheaper per token, gives up 3.1x on context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs Gemma 4 26B A4B — Against Gemma 4 26B A4B (Google), Llama 3.1 70B Instruct costs about 34% more per token and gives up 2x on context. Gemma 4 26B A4B is the one to check first if the price difference matters more than the ceiling.
Price History
Llama 3.1 70B Instruct pricing over time
→0% since May 30
90 data points · tracked daily since May 30, 2026
Ready to try it?
Start using Llama 3.1 70B Instruct
Teams needing capable open-weight LLM performance at budget pricing for coding assistance, summarization, or RAG pipelines.. Start free — no card required.
Claude 3.5 Haiku is Anthropic's fastest and most affordable model in the Claude 3.5 family, designed for high-throughput tasks requiring quick responses without sacrificing Claude's core instruction-following quality. It handles a massive 200K context window while maintaining speed suitable for production pipelines.
Verdict
The fastest way to get Claude's quality in production — just don't confuse 'fast' with 'cheap'.
Quality score
64%
Pricing
$0.80/1M in
$4.00/1M out
Speed
Very fast
5/5 speed
Context
200k tokens
Output cost of $4/1M is notably higher than competing fast/mini models. Input cost at ~$0.80/1M is competitive. Best value emerges in input-heavy pipelines like document classification or RAG retrieval where output tokens are minimal.
High-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
GPT-5 Mini is OpenAI's budget-tier distillation of GPT-5, designed for high-volume, cost-sensitive tasks that don't require full flagship reasoning depth. It supersedes GPT-4o with improved instruction following and a massively expanded 400K context window at a fraction of the cost.
Verdict
The new budget default for OpenAI API users: faster, cheaper, and smarter than GPT-4o with a context window that punches well above its price tier.
Quality score
66%
Pricing
$0.25/1M in
$2.00/1M out
Speed
Very fast
5/5 speed
Context
400k tokens
Output cost of $2/1M tokens is higher than some competing budget models (Gemini Flash at ~$0.60/1M output). At scale, output-heavy tasks may erode cost advantages — monitor token ratios carefully. Supersedes GPT-4o, which may be deprecated on a rolling basis.
BudgetFastLong ContextHigh VolumeOpenAI
Best for
High-volume production workloads — chatbots, summarization pipelines, and document Q&A — where cost efficiency matters more than peak reasoning.
Gemma 4 26B A4B is a sparse mixture-of-experts open model from Google, activating only ~4B parameters per forward pass despite having 26B total parameters. It offers a 262K context window at budget pricing, making it one of the more capable open-weight models for its cost tier.
Verdict
A lean, fast, and surprisingly capable budget model best suited for high-volume text tasks where cost efficiency trumps peak quality.
Quality score
59%
Pricing
$0.13/1M in
$0.40/1M out
Speed
Fast
4/5 speed
Context
262k tokens
As an open-weight model, Gemma 4 26B can also be self-hosted, making API pricing largely irrelevant at scale. The 'A4B' suffix denotes the active parameter count in its MoE configuration. Listed as superseding Gemini 3 Flash Preview, though Gemini 2.0 Flash remains a stronger hosted alternative.
Open-weightBudgetMoELong ContextGoogle
Best for
Cost-sensitive applications needing long-context processing with reasonable quality, such as document summarization pipelines or lightweight coding assistants.
Pricing moves, ranking shifts, and capability updates.
New ModelMar 27, 2026
Meta: Llama 3.1 70B Instruct — added to UseRightAI
Meta: Llama 3.1 70B Instruct (Meta) is now indexed. The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing.
Llama 3.1 70B Instruct costs $0.39999999999999997 per million input tokens and $0.39999999999999997 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $4.80 at list price, before any batch or caching discounts.
What is Llama 3.1 70B Instruct best for?
Llama 3.1 70B Instruct is best for teams needing capable open-weight llm performance at budget pricing for coding assistance, summarization, or rag pipelines.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid Llama 3.1 70B Instruct?
You need state-of-the-art reasoning, nuanced creative writing, or multimodal (image) understanding — upgrade to Llama 3.1 405B or a frontier model instead.
What is a cheaper alternative to Llama 3.1 70B Instruct?
Gemma 4 26B A4B (Google) at $0.13/1M/1M input against Llama 3.1 70B Instruct's $0.40/1M/1M — roughly 34% less per token all in. A lean, fast, and surprisingly capable budget model best suited for high-volume text tasks where cost efficiency trumps peak quality. Compare it first if Llama 3.1 70B Instruct's pricing is the thing stopping you.
What is a faster alternative to Llama 3.1 70B Instruct?
Claude 3.5 Haiku — very fast against Llama 3.1 70B Instruct's fast, with 200k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
Newsletter
Get notified when Llama 3.1 70B Instruct pricing changes
We track pricing daily. When this model drops or spikes, you'll know first.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.