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DeepSeek R1 0528 Distill Qwen3-8B vs Llama Guard 3 1B

DeepSeek R1 0528 Distill Qwen3-8B (2025) and Llama Guard 3 1B (2024) are frontier reasoning models from Alibaba and AI at Meta. DeepSeek R1 0528 Distill Qwen3-8B ships a 160K-token context window, while Llama Guard 3 1B ships a not-yet-sourced context window. On pricing, Llama Guard 3 1B costs $0.1/1M input tokens versus $0.2/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Llama Guard 3 1B is ~100% cheaper at $0.1/1M; pay for DeepSeek R1 0528 Distill Qwen3-8B only for reasoning depth.

Specs

Released2025-01-012024-09-25
Context window160K
Parameters8B1B
Architecturedecoder onlydecoder only
LicenseOpen SourceOpen Source
Knowledge cutoff--

Pricing and availability

DeepSeek R1 0528 Distill Qwen3-8BLlama Guard 3 1B
Input price$0.2/1M tokens$0.1/1M tokens
Output price$0.2/1M tokens$0.1/1M tokens
Providers

Capabilities

DeepSeek R1 0528 Distill Qwen3-8BLlama Guard 3 1B
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on reasoning mode: DeepSeek R1 0528 Distill Qwen3-8B. 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.

For cost, DeepSeek R1 0528 Distill Qwen3-8B lists $0.2/1M input and $0.2/1M output tokens, while Llama Guard 3 1B lists $0.1/1M input and $0.1/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama Guard 3 1B lower by about $0.1 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.

Choose DeepSeek R1 0528 Distill Qwen3-8B when reasoning depth are central to the workload. Choose Llama Guard 3 1B when provider fit and lower input-token cost 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 is cheaper, DeepSeek R1 0528 Distill Qwen3-8B or Llama Guard 3 1B?

Llama Guard 3 1B is cheaper on tracked token pricing. DeepSeek R1 0528 Distill Qwen3-8B costs $0.2/1M input and $0.2/1M output tokens. Llama Guard 3 1B costs $0.1/1M input and $0.1/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is DeepSeek R1 0528 Distill Qwen3-8B or Llama Guard 3 1B open source?

DeepSeek R1 0528 Distill Qwen3-8B is listed under Open Source. Llama Guard 3 1B is listed under Open Source. 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 reasoning mode, DeepSeek R1 0528 Distill Qwen3-8B or Llama Guard 3 1B?

DeepSeek R1 0528 Distill Qwen3-8B has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run DeepSeek R1 0528 Distill Qwen3-8B and Llama Guard 3 1B?

DeepSeek R1 0528 Distill Qwen3-8B is available on Fireworks AI. Llama Guard 3 1B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick DeepSeek R1 0528 Distill Qwen3-8B over Llama Guard 3 1B?

Llama Guard 3 1B is ~100% cheaper at $0.1/1M; pay for DeepSeek R1 0528 Distill Qwen3-8B only for reasoning depth. If your workload also depends on reasoning depth, start with DeepSeek R1 0528 Distill Qwen3-8B; if it depends on provider fit, run the same evaluation with Llama Guard 3 1B.

Continue comparing

Last reviewed: 2026-04-18. Data sourced from public model cards and provider documentation.