LLM Reference

Llama 3.1 Nemotron 70B Reward vs Mistral Small 4

Llama 3.1 Nemotron 70B Reward (2024) and Mistral Small 4 (2026) are compact production models from NVIDIA AI and MistralAI. Llama 3.1 Nemotron 70B Reward ships a 4k-token context window, while Mistral Small 4 ships a 256k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.

Mistral Small 4 fits 64x more tokens; pick it for long-context work and Llama 3.1 Nemotron 70B Reward for tighter calls.

Decision scorecard

Local evidence first
SignalLlama 3.1 Nemotron 70B RewardMistral Small 4
Best forgeneral production evaluationmultimodal apps, tool-calling agents, and provider-routed production
Decision fitClassificationRAG, Agents, and Long context
Context window4k256k
Cheapest output-$0.60/1M tokens
Provider routes1 tracked3 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Llama 3.1 Nemotron 70B Reward when...
  • Local decision data tags Llama 3.1 Nemotron 70B Reward for Classification.
Choose Mistral Small 4 when...
  • Mistral Small 4 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Mistral Small 4 has broader tracked provider coverage for fallback and procurement flexibility.
  • Mistral Small 4 uniquely exposes Vision, Multimodal, and Function calling in local model data.
  • Local decision data tags Mistral Small 4 for RAG, Agents, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Llama 3.1 Nemotron 70B Reward

Unavailable

No complete token price in local provider data

Mistral Small 4

$270

Cheapest tracked route/tier: OpenRouter

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Llama 3.1 Nemotron 70B Reward -> Mistral Small 4
  • Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
  • Mistral Small 4 adds Vision, Multimodal, and Function calling in local capability data.
Mistral Small 4 -> Llama 3.1 Nemotron 70B Reward
  • Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
  • Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.

Specs

Specification
Released2024-10-012026-03-16
Context window4k256k
Parameters70B119B (6.5B active)
Architecturedecoder onlymoe
LicenseNVIDIA Open ModelApache 2.0(OSI)
OpennessOpen weightsOpen source
Commercial useCommercial use allowedCommercial use allowed
Knowledge cutoff-2025-06

Pricing and availability

Pricing attributeLlama 3.1 Nemotron 70B RewardMistral Small 4
Input price-$0.15/1M tokens
Output price-$0.60/1M tokens
Providers

Capabilities

CapabilityLlama 3.1 Nemotron 70B RewardMistral Small 4
VisionNoYes
MultimodalNoYes
ReasoningNoNo
Function callingNoYes
Tool useNoYes
Structured outputsNoNo
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: Mistral Small 4, multimodal input: Mistral Small 4, function calling: Mistral Small 4, and tool use: Mistral Small 4. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

Pricing coverage is uneven: Llama 3.1 Nemotron 70B Reward has no token price sourced yet and Mistral Small 4 has $0.15/1M input tokens. Provider availability is 1 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Llama 3.1 Nemotron 70B Reward when provider fit are central to the workload. Choose Mistral Small 4 when long-context analysis, larger context windows, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, Llama 3.1 Nemotron 70B Reward or Mistral Small 4?

Mistral Small 4 supports 256k tokens, while Llama 3.1 Nemotron 70B Reward supports 4k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Llama 3.1 Nemotron 70B Reward or Mistral Small 4 open source?

Llama 3.1 Nemotron 70B Reward is listed under NVIDIA Open Model. Mistral Small 4 is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for vision, Llama 3.1 Nemotron 70B Reward or Mistral Small 4?

Mistral Small 4 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for multimodal input, Llama 3.1 Nemotron 70B Reward or Mistral Small 4?

Mistral Small 4 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for function calling, Llama 3.1 Nemotron 70B Reward or Mistral Small 4?

Mistral Small 4 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Llama 3.1 Nemotron 70B Reward and Mistral Small 4?

Llama 3.1 Nemotron 70B Reward is available on NVIDIA NIM. Mistral Small 4 is available on OpenRouter, NVIDIA NIM, and Mistral AI Studio. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-14. Data sourced from public model cards and provider documentation.