Llama 3.3 70B vs Qwen3-105B
Llama 3.3 70B (2025) and Qwen3-105B (2025) are compact production models from AI at Meta and Alibaba. Llama 3.3 70B ships a 8k-token context window, while Qwen3-105B ships a 128k-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. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3-105B fits 16x more tokens; pick it for long-context work and Llama 3.3 70B for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.3 70B | Qwen3-105B |
|---|---|---|
| Best for | multimodal apps and tool-calling agents | tool-calling agents |
| Decision fit | Agents, Vision, and Classification | RAG, Agents, and Long context |
| Context window | 8k | 128k |
| Cheapest output | $0.90/1M tokens | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Llama 3.3 70B has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.3 70B uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Llama 3.3 70B for Agents, Vision, and Classification.
- Qwen3-105B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Qwen3-105B 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 3.3 70B
$945
Cheapest tracked route/tier: Fireworks AI
Qwen3-105B
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 3.3 70B and Qwen3-105B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- No overlapping tracked provider route is sourced for Qwen3-105B and Llama 3.3 70B; plan for SDK, billing, or endpoint changes.
- Llama 3.3 70B adds Vision and Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-09 | 2025-12-15 |
| Context window | 8k | 128k |
| Parameters | 70B | 105B |
| 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 | 2024-12 | 2025-02 |
Pricing and availability
| Pricing attribute | Llama 3.3 70B | Qwen3-105B |
|---|---|---|
| Input price | $0.90/1M tokens | - |
| Output price | $0.90/1M tokens | - |
| Providers | - |
Capabilities
| Capability | Llama 3.3 70B | Qwen3-105B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available for this pair.
Deep dive
The capability footprint differs most on vision: Llama 3.3 70B and multimodal input: Llama 3.3 70B. Both models share function calling and tool use, 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 3.3 70B has $0.90/1M input tokens and Qwen3-105B has no token price sourced yet. Provider availability is 1 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 3.3 70B when vision-heavy evaluation and broader provider choice are central to the workload. Choose Qwen3-105B 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. 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Llama 3.3 70B or Qwen3-105B?
Qwen3-105B supports 128k tokens, while Llama 3.3 70B 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 3.3 70B or Qwen3-105B open source?
Llama 3.3 70B is listed under Llama 3 Community. Qwen3-105B 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 3.3 70B or Qwen3-105B?
Llama 3.3 70B 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 3.3 70B or Qwen3-105B?
Llama 3.3 70B 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 3.3 70B or Qwen3-105B?
Both Llama 3.3 70B and Qwen3-105B expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run Llama 3.3 70B and Qwen3-105B?
Llama 3.3 70B is available on Fireworks AI. Qwen3-105B 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: 2026-04-18. Data sourced from public model cards and provider documentation.