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Llama 3.3 70B Instruct

Chat model by Meta.

Meta language model featuring a 128,000-token context window. Engineered for dialogue, instruction following, and versatile text generation.

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Models / Llama 3.3 70B InstructID: azure_ai/Llama-3.3-70B-Instruct

Maker

Meta

Served On: Azure Foundry · Also served via AWS Bedrock

Specifications

Context window, ranked against other chat models

Proportional scale: 0 to 1.05M tokens. Not ranked, no published context window: qwen-plus-2025-09-11, voxtral-small-24b-2507, qwen3.6-plus-2026-04-02.

Context window

—

Max output

—

Modality

chat

Vision
Function calling
System prompt

Benchmarks & performance rankings

Empirical intelligence benchmarks and category leaderboard placements for Llama 3.3 70B Instruct.

Compare in BenchmarksLeaderboard
Leaderboard placements
#12
speedScore: 93.2%
Core benchmark dimensionsSource: Meta AI Benchmark Report
MMLUKnowledge

86%

HumanEvalCoding

84.5%

Latency p50TTFT

310ms

p99: 850ms

ThroughputStream

98tok/s

SWE-bench: 33%

Recommended by nRouter Smart Routing: Predictable private SLA workloads. Key operational strength: 310ms p50 latency on optimized vLLM backends.

Pricing

Rates are read live

Pricing for Llama-3.3-70B-Instruct is fetched from the live catalogue on load, so it is never served from a cache that could outlive a repricing.

Availability

Not enough data yet

We have not collected enough health probes for this model to publish an uptime or latency figure. A percentage from a handful of samples is fabricated precision, so none is shown until the sample count supports one.

How Llama 3.3 70B Instruct is billed

Billed at the provider's list price, with no per-token markup.

Exact list price
Tokens settle at the published rate of the model that served your call.
Cache discounts passed through
Provider prompt-caching discounts reach your balance with no added fee.
Cost on every response
Each response carries the x-nr-request-cost header, so you can reconcile spend per request.

More from Meta

muse-spark-1.1muse-spark-1.3-contributorNousResearch/Hermes-3-Llama-3.1-70Bmeta-models/Muse-Glimmer-30B

Start building with Llama-3.3-70B-Instruct

One API key, three steps. Choose your tool to begin.

  1. Get a key

    Create API key

  2. Set your API key

    export NROUTER_API_KEY="<YOUR_API_KEY>"

  3. Configure Claude Code

    Choose a tool, then copy its setup from the panel.

npm install -g @anthropic-ai/claude-code
Claude Code · azure_ai/Llama-3.3-70B-Instruct
# Point Claude Code to nRouter gateway
export ANTHROPIC_BASE_URL="https://api.nrouter.ai"
export ANTHROPIC_API_KEY="$NROUTER_API_KEY"

# Launch with azure_ai/Llama-3.3-70B-Instruct
claude --model azure_ai/Llama-3.3-70B-Instruct

FAQs

What is Llama 3.3 70B Instruct?

Meta language model featuring a 128,000-token context window. Engineered for dialogue, instruction following, and versatile text generation.

How much does Llama 3.3 70B Instruct cost?

Llama 3.3 70B Instruct is billed at the provider's list price on nRouter with no per-token markup — tokens settle at the exact upstream rate. Input and output rates are read live, so see the pricing section above for the current figures.

Does Llama 3.3 70B Instruct support tool calling and structured outputs?

Yes. Llama 3.3 70B Instruct supports tool calling (function calling), so it can invoke external tools and APIs during a conversation.

What inputs and outputs does Llama 3.3 70B Instruct support?

Llama 3.3 70B Instruct takes text as input and returns text as output.

How do I use Llama 3.3 70B Instruct via nRouter?

Use any OpenAI-compatible client: point it at https://api.nrouter.ai/v1 with your nRouter API key and set the model to "azure_ai/Llama-3.3-70B-Instruct". The quick-start panel above has a copy-paste curl command for this model.

When was Llama 3.3 70B Instruct released?

Llama 3.3 70B Instruct was released on August 30, 2026.

Where is the Llama 3.3 70B Instruct documentation?

Start at nrouter.ai/docs. The quick-start guide (nrouter.ai/docs/getting-started/quick-start) covers API keys and your first request, and the API reference (nrouter.ai/docs/api-reference) lists every parameter.

What other text models does Meta have?

Other Meta text models on nRouter include Muse Spark 1.1 (nrouter.ai/models/muse-spark-1.1), Muse Spark 1.3 Contributor (nrouter.ai/models/muse-spark-1.3-contributor), Hermes 3 Llama 3.1 70B (nrouter.ai/models/nousresearch-hermes-3-llama-3.1-70b), Muse Glimmer 30B (nrouter.ai/models/meta-models-muse-glimmer-30b).