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Meta's open-weights, dense 70B instruct model with a 128k-token context window, built for self-hosted or Sovereign Inference.
Overview. Llama-3.3-70B-Instruct is Meta's dense, 70-billion-parameter instruction-tuned language model, released in December 2024 as an open-weights successor in the Llama 3 line. It pairs a 128k-token context window with text-in, text-out generation, and is distributed under Meta's Llama 3.3 Community License for self-hosting or third-party Inference. Developers reach for it when they want a large-context, general-purpose instruct model without depending on a closed API.
Key strengths. As a dense 70B transformer with grouped-query attention, it avoids the routing complexity of mixture-of-experts designs while still supporting a full 128k-token context for long documents and extended conversations. Its Llama 3 chat template includes explicit system-role support and built-in tool-calling formatting, making it a solid fit for structured, multi-turn or agentic prompting. No independent benchmark scores were available in research for this model, so quality-tier and domain-specific performance claims are intentionally omitted here.
When to use it. Use this model for long-context tasks — large document analysis, extended multi-turn chats, or workflows that need a system prompt to set persistent behavior. It's a good candidate when tool-calling style output is required, since the Llama 3 instruct template natively supports structured function-call formatting. Because it's open-weights, it also suits teams that want to run Inference on their own infrastructure or through a Sovereign-hosted endpoint rather than a single vendor's closed API.
Limitations. The model is text-only — it has no vision, audio input, or audio output capability. Its training data cutoff is December 2023, so it won't have knowledge of more recent events. It also has no extended-thinking or reasoning-trace mode, and maximum completion-token limits vary by hosting provider rather than being fixed by Meta at the model level.
Sovereignty note. Llama-3.3-70B-Instruct is created by Meta, a US company, but its open-weights distribution means it can be deployed on Sovereign, EU-resident infrastructure rather than routed through a US-operated API, avoiding CLOUD Act exposure when hosted that way.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L3 | 0,90 / 0,90€ | Online | |||
| L1 | 0,65 / 0,65€ | Online |
15 use cases classified by the curator. The note says why.
Tool-calling is a documented capability, directly matching function-calling app requirements; open-weights licensing lets teams pin the exact model version for production reliability.
Explicit tool-calling capability at 70B scale — Llama 3.3 is Meta's strongest open-weights variant for orchestrating external tool calls, and its open-weights status enables self-hosted agent infrastructure.
Sovereign deployability is the key differentiator for audit workflows requiring data residency guarantees; capability profile otherwise unconfirmed pending catalogue completion.
Tool-calling supports action-taking agents, but unconfirmed context window and lack of published agentic benchmarks limit this to acceptable rather than strong.
70B instruct model handles structured classification tasks reasonably; tool-calling can enforce schema-constrained outputs.
General-purpose 70B instruct model suitable for conversational support; open-weights status allows on-prem deployment for teams avoiding US-jurisdiction API exposure.
Standard instruct-tuned capability at 70B scale covers routine drafting tasks adequately, though no curated benchmark evidence is yet available for this variant.
Tool-calling capability supports structured entity extraction workflows despite the model card lacking extraction-specific benchmarks.
No vision capability is listed for this model — text-only tool-calling profile cannot support image-based Q&A.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, ifbench, aa-multilingual-index, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/llama-3-3-70b-instruct",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at