A capable but now-outdated open-weight model undercut by its tiny context window and newer successors.
72
Coding
68
Writing
60
Research
0
Images
62
Value
15
Long Context
Use this when
Developers and researchers who need a capable open-weight model for coding, analysis, and instruction-following tasks at a mid-range price point.
Skip this if
You need to process long documents, codebases, or conversations — the 8K context window will truncate almost any real-world task requiring extended context.
Pricing
$0.51/1M in
$0.74/1M out
→0%since May 2026
Context
8k tokens
Speed
Balanced
This is the original Llama 3 70B, not the 3.1 or 3.3 variants. Llama 3.1 70B offers a 128K context window at comparable pricing and is strongly preferred. Consider this model only if you have a specific reason to pin to the original Llama 3 checkpoint.
Strong instruction-following with notably improved accuracy over Llama 2 70B
Competitive coding performance on Python and common languages, rivaling early GPT-3.5-level outputs
Open weights allow fine-tuning and self-hosting, giving flexibility unavailable with closed models
Good multilingual capability across English, German, French, Spanish, and other major languages
Weaknesses
Tiny 8K context window is severely limiting compared to competitors like Gemini 3.1 Pro (1M tokens) or Claude Sonnet 4.6 (200K tokens)
Has been superseded by Llama 3.1 and Llama 3.3 variants which offer better performance and larger context
Struggles with complex multi-step reasoning tasks compared to frontier models like GPT-5.4 or Claude Sonnet 4.6
Real-world use cases
What people actually use Meta: Llama 3 70B Instruct for.
Writing and debugging Python scripts for data processing pipelines
Summarizing short articles or reports that fit within the 8K token limit
Answering structured Q&A queries or generating formatted JSON outputs from brief inputs
Price History
Meta: Llama 3 70B Instruct pricing over time
→0% since May 9
89 data points · tracked daily since May 9, 2026
Ready to try it?
Start using Meta: Llama 3 70B Instruct
Developers and researchers who need a capable open-weight model for coding, analysis, and instruction-following tasks at a mid-range price point.. Start free — no card required.
Meta Superintelligence Labs' first closed frontier model — a natively multimodal agentic reasoner (text, image, video, audio, PDF in) with a parallel-agent 'Contemplating mode', priced aggressively below rivals.
Verdict
Best-value multimodal agentic model — GPT-5.5-tier smarts, video/audio/PDF in.
Quality score
89%
Pricing
$1.25/1M in
$4.25/1M out
Speed
Balanced
Best for agentic tool-use and multimodal reasoning at aggressive pricing
Context
1.0M tokens
v1.0 launched April 8, 2026 alongside Llama 5; v1.1 (July 9) opened the paid API; v1.2 (Aug 5) is coding-focused and powers Muse Code. Built with 'over an order of magnitude less' pretraining compute than Llama 4 Maverick. Cache hits $0.15/1M.
MultimodalAgenticValue1M context
Best for
Agentic tool-use and multimodal reasoning at aggressive pricing
Claude 3.5 Sonnet is Anthropic's mid-cycle flagship model, balancing strong reasoning, coding, and instruction-following with a 200K context window. It sits between Haiku and Opus in Anthropic's lineup, offering near-flagship quality at a lower cost than top-tier models.
Verdict
One of the best models for coding and complex instruction-following, but its premium pricing demands premium use cases.
Quality score
81%
Pricing
$6.00/1M in
$30.00/1M out
Speed
Balanced
Best for complex coding tasks, multi-step reasoning, and long-document analysis where gpt-4o-class quality is needed without paying for the absolute top tier.
Context
200k tokens
Pricing at $6 input / $30 output per million tokens is significantly higher than GPT-4o ($2.50/$10). Best accessed via Anthropic API or Amazon Bedrock. Claude 3.5 Sonnet (October 2024 version) supersedes the June 2024 release with improved performance.
Claude Opus 4 is Anthropic's most capable flagship model, designed for complex reasoning, nuanced writing, and sophisticated multi-step tasks. It sits at the top of the Claude 4 family, prioritizing depth and quality over speed.
Verdict
Anthropic's best model for when quality matters more than speed or cost.
Quality score
84%
Pricing
$10.00/1M in
$50.00/1M out
Speed
Deliberate
Best for demanding professional tasks requiring deep reasoning, nuanced judgment, and high-quality long-form output.
Context
200k tokens
At $15 input / $75 output per 1M tokens, Opus 4 is one of the most expensive models available. Anthropic recommends using Claude Sonnet 4 for most production use cases and reserving Opus 4 for tasks explicitly requiring maximum capability.
FlagshipPremiumReasoningLong ContextAgentic
Best for
Demanding professional tasks requiring deep reasoning, nuanced judgment, and high-quality long-form output.
Pricing moves, ranking shifts, and capability updates.
New ModelMar 27, 2026
Meta: Llama 3 70B Instruct — added to UseRightAI
Meta: Llama 3 70B Instruct (Meta) is now indexed. A capable but now-outdated open-weight model undercut by its tiny context window and newer successors.
Meta: Llama 3 70B Instruct is best for developers and researchers who need a capable open-weight model for coding, analysis, and instruction-following tasks at a mid-range price point.. It is a strong fit when that workflow matters more than the tradeoffs around balanced pricing and balanced speed.
When should I avoid Meta: Llama 3 70B Instruct?
You need to process long documents, codebases, or conversations — the 8K context window will truncate almost any real-world task requiring extended context.
What is a cheaper alternative to Meta: Llama 3 70B Instruct?
Mistral: Mistral Nemo 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 70B Instruct?
Muse Spark is the better pick when response time matters more than maximum depth or premium quality.
Newsletter
Get notified when Meta: Llama 3 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.