Llama 3.3 70B on Groq vs Together AI
Groq and Together AI both host Llama 3.3 70B Instruct. This page compares their public canonical-prompt benchmark results on Benchscope. Score differences between providers reflect infrastructure, quantization, and serving configuration — not underlying model differences.
Why the Same Model Can Produce Different Results
A model family name refers to the underlying published weights. But when you call an inference API, you are talking to a provider-hosted endpoint with its own hardware, quantization level, batching strategy, and configuration. Benchscope records each provider's endpoint separately so these differences are visible.
What to Compare
Compare MATH and MMLU scores across providers to assess whether the hosting environment degrades reasoning performance. MATH is particularly sensitive to serving differences — multi-step reasoning is more affected by quantization than factual recall. Latency metrics reflect Groq's LPU throughput advantage alongside accuracy.
Caveats
Benchmark scores are directional, not universal. A provider that scores higher on MATH may not perform better for your specific use case. Provider configurations change over time — results on Benchscope reflect endpoint behavior at the time each run was submitted.
Related
- Llama 3.3 70B model page — all providers and benchmarks
- Groq benchmark results
- Together AI benchmark results
- MATH benchmark results across all providers
- MMLU benchmark results across all providers
- GSM8K benchmark results across all providers
- Best LLM endpoint for MMLU
- Best LLM endpoint for MATH
- Best LLM endpoint for GSM8K
- How endpoints, runs, and comparability are defined
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