Anthropic's model for the most demanding reasoning and long-horizon agentic work. 1M-token context window, up to 128K output tokens, adaptive thinking that is always on.
The most capable model of Anthropic's Opus 4 series. State of the art on long-horizon agentic work, coding and knowledge tasks, with a 1M-token context window at standard pricing.
Anthropic's current Opus model for long-running agentic coding and knowledge work. 1M-token context window, up to 128K output tokens, adaptive thinking that is always on.
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About Llama 3.3 70B
TL;DRAs of September 23, 2026
Llama 3.3 70B is a model by Meta in the Text & chat category. Llama 3.3 70B is currently not available on Railwail. The context window holds 131,072 tokens, and one response can be up to 4,096 tokens long.
Background
About Meta AI
Founded 2013 · Menlo Park, USA
Meta AI (originally Facebook AI Research, FAIR) was founded in December 2013 by Mark Zuckerberg with Yann LeCun as its first director. The lab is now part of Meta Platforms and houses several thousand researchers across Menlo Park, New York, Paris, Montreal, Seattle and Tel Aviv. FAIR has authored landmark papers including PyTorch (2017), Detectron, fastText, RoBERTa, the original LLaMA paper (Feb 2023), Llama 2 (Jul 2023), Llama 3 (Apr 2024), Llama 3.1 (Jul 2024, including the 405B flagship), Llama 3.2 (Sep 2024, multimodal and on-device sizes), Llama 3.3 (Dec 2024) and Llama 4 (Apr 2025). Meta's open-weights strategy under the Llama Community License has made Llama by far the most widely deployed open-weight family with over 700M cumulative downloads. Yann LeCun, Joelle Pineau and Ahmad Al-Dahle lead the GenAI organisation that productises the work. Beyond Llama, Meta ships SeamlessM4T for speech, Segment Anything for vision and the Meta AI consumer assistant across WhatsApp, Instagram, Messenger and Meta.ai. Meta is also the largest user of NVIDIA H100 GPUs in industry, with reported cluster sizes above 350,000 H100-equivalents.
Llama 3.3 70B Instruct was released by Meta on 6 December 2024 as the final 3.x release before Llama 4. It is a dense decoder-only Transformer with 70 billion parameters, 80 layers, 64 query heads, 8 KV heads (Grouped Query Attention) and a tokenizer with 128K vocabulary. The notable claim is that the post-training recipe lifts the 70B model to match or exceed Llama 3.1 405B on most benchmarks at roughly 1/6th of the inference cost. Llama 3.3 reuses the Llama 3.1 pretrained base (which was trained on approximately 15 trillion tokens of curated public web data, code, books and licensed datasets, with a December 2023 knowledge cutoff). The improvement comes from an updated post-training pipeline combining new supervised fine-tuning, rejection sampling, Direct Preference Optimisation (DPO), online reinforcement learning, and synthetic instruction data generated by Llama 3.1 405B and other models. Llama 3.3 supports 128K context, function calling, parallel tool calls, JSON output and the official Llama 3 chat template. The model is text-only (vision sits in the Llama 3.2 family) and ships under the Llama 3.3 Community License which permits commercial use except for products with more than 700 million monthly active users at launch.
Parameters
70B (dense)
Context
128,000 tokens
Capabilities
70B dense parameters with Grouped Query Attention
Post-training lifts 70B to roughly match Llama 3.1 405B on key benchmarks
128K context window
Function calling and parallel tool calls (Llama 3.1+ style)
JSON output with the chat template tool format
Multilingual: 8 officially supported languages including English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
Code generation across major programming languages
Best for: open-weight chat, function calling, on-prem enterprise, cost-efficient replacement for 405B.
Training & license
Reuses the Llama 3.1 pretrained base (15T tokens of curated public web data, code, books and licensed data, with a December 2023 cutoff). Post-training applies SFT, rejection sampling, DPO, online RL and synthetic data from Llama 3.1 405B.
License: Llama 3.3 Community License: open weights, commercial use permitted, with a >700M MAU clause that requires a separate license from Meta.
Safety testing: Meta applies SFT and RL safety stages plus a 'cybersec' and 'malicious code' evaluation pipeline. The Llama 3 family is among the most safety-tested open-weight model lines, with published Responsible Use Guides and the Llama Guard 3 companion classifier.
Known limitations
Text-only (no native vision; use Llama 3.2 Vision variants)
Knowledge cutoff December 2023
Tool-calling format is bespoke and requires the official chat template
Community License has a >700M MAU restriction
Long context recall degrades beyond ~64K on some tasks
Llama 3.3 Community License: open weights, commercial use permitted, with a >700M MAU clause that requires a separate license from Meta.
Catalog entry updated
September 23, 2026
Tags
open-source
popular
07
Example prompts
Examples from the Railwail catalog. They were not generated live on this page.
Comparison Essay
Compare and contrast REST and GraphQL APIs in terms of flexibility and performance
Show example answer
REST APIs use fixed endpoints that return predetermined data structures, which is simple but can lead to over-fetching (getting more data than needed) or under-fetching (requiring multiple requests). GraphQL lets clients request exactly the fields they need in a single query, offering superior flexibility. However, REST benefits from better HTTP caching, simpler error handling, and wider tooling support. GraphQL shines in complex applications with varied data needs, while REST remains ideal for straightforward CRUD operations and public APIs.
Explain Like I'm 5
Explain how the internet works to a 5-year-old
Show example answer
Imagine you want to send a drawing to your friend who lives far away. You put your drawing in a magic mailbox, and the mailbox breaks it into tiny puzzle pieces. These pieces zoom through special tunnels underground and even under the ocean! When all the pieces reach your friend's magic mailbox, it puts the puzzle back together. That's basically how the internet works—your computer breaks messages into tiny pieces, sends them through wires, and the other computer puts them back together super fast.
Llama 3.3 70B is a model by Meta in the Text & chat category. It is listed on Railwail but cannot be run at the moment.
How much does Llama 3.3 70B cost on Railwail?
Llama 3.3 70B cannot be run on Railwail at the moment, so there is no current price. Available alternatives with prices are listed further down this page.
What is the context window of Llama 3.3 70B?
The context window of Llama 3.3 70B holds 131,072 tokens. One response can be up to 4,096 tokens long.
How fast is Llama 3.3 70B?
There are not enough measured runs of Llama 3.3 70B on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.
Is Llama 3.3 70B better than Claude Fable 5.1?
That depends on the task. Llama 3.3 70B (Meta) and Claude Fable 5.1 (Anthropic) are both models in the Text & chat category. The comparison page shows their prices and specifications side by side.
