Gemma 3n 4B (free) vs Llama 3.1 8B Instruct
Gemma 3n 4B (free) (2025) and Llama 3.1 8B Instruct (2024) are compact production models from Google DeepMind and AI at Meta. Gemma 3n 4B (free) ships a 8K-token context window, while Llama 3.1 8B Instruct ships a 128K-token context window. On pricing, Gemma 3n 4B (free) costs $0.02/1M input tokens versus $0.02/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 8B Instruct fits 16x more tokens; pick it for long-context work and Gemma 3n 4B (free) for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemma 3n 4B (free) | Llama 3.1 8B Instruct |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | RAG, Long context, and Classification |
| Context window | 8K | 128K |
| Cheapest output | $0.04/1M tokens | $0.05/1M tokens |
| Provider routes | 3 tracked | 12 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemma 3n 4B (free) has the lower cheapest tracked output price at $0.04/1M tokens.
- Local decision data tags Gemma 3n 4B (free) for Classification and JSON / Tool use.
- Llama 3.1 8B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 8B Instruct for RAG, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemma 3n 4B (free)
$26.00
Cheapest tracked route: Together AI
Llama 3.1 8B Instruct
$28.50
Cheapest tracked route: OpenRouter
Estimated monthly gap: $2.50. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Together AI, NVIDIA NIM, and OpenRouter; start route-level A/B tests there.
- Llama 3.1 8B Instruct is $0.01/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on NVIDIA NIM, Together AI, and OpenRouter; start route-level A/B tests there.
- Gemma 3n 4B (free) is $0.01/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-03 | 2024-07-23 |
| Context window | 8K | 128K |
| Parameters | — | 8B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Gemma 3n 4B (free) | Llama 3.1 8B Instruct |
|---|---|---|
| Input price | $0.02/1M tokens | $0.02/1M tokens |
| Output price | $0.04/1M tokens | $0.05/1M tokens |
| Providers |
Capabilities
| Capability | Gemma 3n 4B (free) | Llama 3.1 8B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Gemma 3n 4B (free) lists $0.02/1M input and $0.04/1M output tokens, while Llama 3.1 8B Instruct lists $0.02/1M input and $0.05/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemma 3n 4B (free) lower by about $0 per million blended tokens. Availability is 3 providers versus 12, so concentration risk also matters.
Choose Gemma 3n 4B (free) when provider fit are central to the workload. Choose Llama 3.1 8B Instruct 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, Gemma 3n 4B (free) or Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct supports 128K tokens, while Gemma 3n 4B (free) supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Gemma 3n 4B (free) or Llama 3.1 8B Instruct?
Gemma 3n 4B (free) is cheaper on tracked token pricing. Gemma 3n 4B (free) costs $0.02/1M input and $0.04/1M output tokens. Llama 3.1 8B Instruct costs $0.02/1M input and $0.05/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemma 3n 4B (free) or Llama 3.1 8B Instruct open source?
Gemma 3n 4B (free) is listed under Open Source. Llama 3.1 8B Instruct 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 structured outputs, Gemma 3n 4B (free) or Llama 3.1 8B Instruct?
Both Gemma 3n 4B (free) and Llama 3.1 8B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Gemma 3n 4B (free) and Llama 3.1 8B Instruct?
Gemma 3n 4B (free) is available on NVIDIA NIM, Together AI, and OpenRouter. Llama 3.1 8B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and GroqCloud. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Gemma 3n 4B (free) over Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct fits 16x more tokens; pick it for long-context work and Gemma 3n 4B (free) for tighter calls. If your workload also depends on provider fit, start with Gemma 3n 4B (free); if it depends on long-context analysis, run the same evaluation with Llama 3.1 8B Instruct.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.