Llama 3 8B Instruct vs Qwen3.5-4B
Llama 3 8B Instruct (2024) and Qwen3.5-4B (2026) are compact production models from AI at Meta and Alibaba. Llama 3 8B Instruct ships a 8k-token context window, while Qwen3.5-4B ships a 262k-token context window. On MMLU PRO, Qwen3.5-4B leads by 38.6 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen3.5-4B fits 33x more tokens; pick it for long-context work and Llama 3 8B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3 8B Instruct | Qwen3.5-4B |
|---|---|---|
| Best for | provider-routed production | multimodal apps |
| Decision fit | Coding, Classification, and JSON / Tool use | Coding, Agents, and Long context |
| Context window | 8k | 262k |
| Cheapest output | $0.04/1M tokens | - |
| Provider routes | 17 tracked | 0 tracked |
| Shared benchmarks | 2 rows | MMLU PRO leader |
Decision tradeoffs
- Llama 3 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3 8B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3 8B Instruct for Coding, Classification, and JSON / Tool use.
- Qwen3.5-4B holds a shared-benchmark lead on MMLU PRO, ahead by 38.6 points.
- Qwen3.5-4B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-4B uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Qwen3.5-4B for Coding, 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 8B Instruct
$34.00
Cheapest tracked route/tier: OpenRouter
Qwen3.5-4B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3 8B Instruct and Qwen3.5-4B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- Qwen3.5-4B adds Vision and Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-4B and Llama 3 8B Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Llama 3 8B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-18 | 2026-03-02 |
| Context window | 8k | 262k |
| Parameters | 8B | 4B |
| Architecture | decoder only | - |
| License | Llama 3 Community | Apache 2.0(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2023-03 | - |
Pricing and availability
| Pricing attribute | Llama 3 8B Instruct | Qwen3.5-4B |
|---|---|---|
| Input price | $0.03/1M tokens | - |
| Output price | $0.04/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Llama 3 8B Instruct | Qwen3.5-4B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 3 8B Instruct | Qwen3.5-4B |
|---|---|---|
| MMLU PRO | 40.5 | 79.1 |
| Google-Proof Q&A | 44.8 | 76.2 |
Deep dive
On shared benchmark coverage, MMLU PRO has Llama 3 8B Instruct at 40.5 and Qwen3.5-4B at 79.1, with Qwen3.5-4B ahead by 38.6 points; Google-Proof Q&A has Llama 3 8B Instruct at 44.8 and Qwen3.5-4B at 76.2, with Qwen3.5-4B ahead by 31.4 points. The largest visible gap is 38.6 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Qwen3.5-4B, multimodal input: Qwen3.5-4B, and structured outputs: Llama 3 8B Instruct. 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 8B Instruct has $0.03/1M input tokens and Qwen3.5-4B has no token price sourced yet. Provider availability is 17 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3 8B Instruct when provider fit and broader provider choice are central to the workload. Choose Qwen3.5-4B when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Llama 3 8B Instruct or Qwen3.5-4B?
Qwen3.5-4B supports 262k tokens, while Llama 3 8B Instruct supports 8k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3 8B Instruct or Qwen3.5-4B open source?
Llama 3 8B Instruct is listed under Llama 3 Community. Qwen3.5-4B 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 8B Instruct or Qwen3.5-4B?
Qwen3.5-4B 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Llama 3 8B Instruct or Qwen3.5-4B?
Qwen3.5-4B 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 structured outputs, Llama 3 8B Instruct or Qwen3.5-4B?
Llama 3 8B Instruct has the clearer documented structured outputs signal in this comparison. If structured outputs 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 8B Instruct and Qwen3.5-4B?
Llama 3 8B Instruct is available on AWS Bedrock, DeepInfra, OctoAI API (Deprecated), Fireworks AI, and Alibaba Cloud PAI-EAS. Qwen3.5-4B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.