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 Meta: 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
Price History
Meta: Llama 3.1 70B Instruct pricing over time
→0% since May 9
48 data points · tracked daily since May 9, 2026
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Start using Meta: 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.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
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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.
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.
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A lean, fast, and surprisingly capable budget model best suited for high-volume text tasks where cost efficiency trumps peak quality.
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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.
Gemma 4 31B is Google's open-weight instruction-tuned model offering a strong balance of capability and cost efficiency at just $0.14/$0.40 per million tokens. It features a 262K context window and is designed for developers who need capable on-premise or API-hosted inference without flagship pricing.
Verdict
A well-priced, long-context open-weight model that's ideal for high-volume developer workloads but won't match frontier models on complex reasoning.
Quality score
66%
Pricing
$0.14/1M in
$0.40/1M out
Speed
Fast
Best for cost-conscious developers needing a capable open-weight model for coding assistance, summarization, and document analysis at scale.
Context
262k tokens
As an open-weight model, Gemma 4 31B can be self-hosted via Ollama or Hugging Face in addition to Google's API. Pricing shown is for hosted inference. No image input capability confirmed at launch.
Open WeightBudgetLong ContextCodingSelf-Hostable
Best for
Cost-conscious developers needing a capable open-weight model for coding assistance, summarization, and document analysis at scale.
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.
Meta: 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 Meta: 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 Meta: Llama 3.1 70B Instruct?
Meta: Llama 3.1 8B Instruct is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
What is a faster alternative to Meta: Llama 3.1 70B Instruct?
Anthropic: Claude 3.5 Haiku is the better pick when response time matters more than maximum depth or premium quality.
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