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SambaNova Pricing Breakdown: Cost Per Token & Model Comparison

Deploybase · October 15, 2025 · LLM Pricing

Contents

Sambanova Pricing: SambaNova API: What It Is

Sambanova Pricing is the focus of this guide. SambaNova is inference-only on custom silicon. Reconfigurable dataflow architecture. Cheaper than OpenAI or Anthropic.

No fine-tuning. Inference only. Models available:

  • Llama 3.1 (7B, 70B, 405B variants)
  • Mistral
  • Other open-source models

Speed: SambaNova hardware is optimized for specific models. Better throughput per watt than GPUs.

Pricing structure: input vs output tokens

Input: $0.10/MTok (Llama 3.1 8B) Output: $0.20/MTok (Llama 3.1 8B)

Output is 2x more expensive. Why? Output generation is slower, memory bandwidth limited.

Example usage:

  • Input: 2,000 tokens (prompt) at $0.10/MTok = $0.0002
  • Output: 500 tokens (response) at $0.20/MTok = $0.0001
  • Total: $0.0003 per inference

Compare to OpenAI API pricing:

  • GPT-4o: $2.50/MTok input, $10/MTok output
  • Cost per same request: $0.0050 + $0.0050 = $0.0100

SambaNova at significantly lower cost for this workload.

Cost per token by model

ModelInput $/MTokOutput $/MTokEffective $/1K tokens*
Llama 3.1 8B$0.10$0.20$0.00014
Llama 3.3 70B$0.45$0.90$0.00063
Llama 4 Maverick$0.63$1.80$0.00110

*Assuming 60% input, 40% output tokens in typical workload.

Larger models cost more. Output tokens cost roughly 2x more than input tokens.

As of March 2026, SambaNova pricing undercuts major API providers by 5-7x on average.

Comparison: SambaNova vs OpenAI vs Anthropic

SambaNova on Llama 3.3 70B: $0.00063 effective cost OpenAI GPT-4o: $0.00500 effective cost Anthropic Claude Sonnet 4.6: $0.00180 effective cost

SambaNova Llama 3.3 70B: cheapest option. Claude Sonnet 4.6: similar price, better reasoning. GPT-4o: expensive, strongest model.

For simple text generation: SambaNova wins on cost. For reasoning, analysis, code generation: Claude or GPT-4o better quality.

Speed differences matter. SambaNova claims 5-10x faster output token generation than GPU-based APIs due to custom silicon.

Latency: SambaNova <200ms time-to-first-token. OpenAI GPT-4o: 200-500ms. Matters for interactive applications.

When SambaNova makes sense

Use SambaNova when:

  • Throughput and cost dominate quality requirements
  • Latency under 200ms required for end-user experience
  • Running Llama 3.3/3.1 or Mistral models
  • Budget constraints prevent OpenAI/Anthropic usage
  • Inference-only workload (no fine-tuning)

Skip SambaNova when:

  • Quality of reasoning matters (pick Anthropic Claude)
  • Custom model training needed
  • Vision/image understanding required
  • Closed-source model preferences

SambaNova doesn't offer GPT-equivalent reasoning. Llama 70B vs Claude Sonnet 4.6 performance gap is real for complex tasks.

FAQ

Q: Is SambaNova's custom silicon just marketing? No. Reconfigurable dataflow is real. Performance benchmarks published. Throughput gains verified independently. Speed claims hold up.

Q: Can I fine-tune models on SambaNova? Not currently. Inference-only service. Fine-tuning requires moving workload to RunPod, Lambda, or training infrastructure.

Q: What if SambaNova goes out of business? Risk exists. Company is well-funded. No indication of trouble. But you're dependent on their API. Portable solution: keep inference code API-agnostic, switch providers if needed.

Q: Are output tokens really 2x more expensive than input tokens? Yes. Generation speed is memory-bound. Decoding one token requires reading model weights. Encoding batch reads are more efficient. Economics match hardware realities.

Q: How does SambaNova compare to Groq? Groq uses different silicon. Similar speed claims. Overlapping model availability. Pricing within 20% of each other. Both are inference-only, both cheap.

Q: Should I lock my application into SambaNova? Abstract the provider. Use a provider-agnostic SDK. Claude client libraries, LangChain, LiteLLM all support multiple providers. Switching takes minutes.

Sources