Llama 3.1 Swallow 70B Instruct vs Qwen2-72B
Llama 3.1 Swallow 70B Instruct (2025) and Qwen2-72B (2024) are compact production models from Tokyo Institute of Technology and Alibaba. Llama 3.1 Swallow 70B Instruct ships a 4K-token context window, while Qwen2-72B ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen2-72B fits 32x more tokens; pick it for long-context work and Llama 3.1 Swallow 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 Swallow 70B Instruct | Qwen2-72B |
|---|---|---|
| Decision fit | General | Coding, RAG, and Long context |
| Context window | 4K | 128K |
| Cheapest output | - | $0.65/1M tokens |
| Provider routes | 1 tracked | 4 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Llama 3.1 Swallow 70B Instruct when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Qwen2-72B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2-72B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen2-72B uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen2-72B for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Llama 3.1 Swallow 70B Instruct
Unavailable
No complete token price in local provider data
Qwen2-72B
$523
Cheapest tracked route: DeepInfra
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Llama 3.1 Swallow 70B Instruct and Qwen2-72B; plan for SDK, billing, or endpoint changes.
- Qwen2-72B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Qwen2-72B and Llama 3.1 Swallow 70B Instruct; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2024-06-05 |
| Context window | 4K | 128K |
| Parameters | 70B | 72.71B |
| Architecture | decoder only | decoder only |
| License | 1 | Apache 2.0 |
| Knowledge cutoff | 2023 | - |
Pricing and availability
| Pricing attribute | Llama 3.1 Swallow 70B Instruct | Qwen2-72B |
|---|---|---|
| Input price | - | $0.45/1M tokens |
| Output price | - | $0.65/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.1 Swallow 70B Instruct | Qwen2-72B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Qwen2-72B. 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 Swallow 70B Instruct has no token price sourced yet and Qwen2-72B has $0.45/1M input tokens. Provider availability is 1 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 Swallow 70B Instruct when provider fit are central to the workload. Choose Qwen2-72B 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. 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 3.1 Swallow 70B Instruct or Qwen2-72B?
Qwen2-72B supports 128K tokens, while Llama 3.1 Swallow 70B Instruct 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 Swallow 70B Instruct or Qwen2-72B open source?
Llama 3.1 Swallow 70B Instruct is listed under 1. Qwen2-72B 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 structured outputs, Llama 3.1 Swallow 70B Instruct or Qwen2-72B?
Qwen2-72B 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 3.1 Swallow 70B Instruct and Qwen2-72B?
Llama 3.1 Swallow 70B Instruct is available on NVIDIA NIM. Qwen2-72B is available on Fireworks AI, DeepInfra, Together AI, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 Swallow 70B Instruct over Qwen2-72B?
Qwen2-72B fits 32x more tokens; pick it for long-context work and Llama 3.1 Swallow 70B Instruct for tighter calls. If your workload also depends on provider fit, start with Llama 3.1 Swallow 70B Instruct; if it depends on long-context analysis, run the same evaluation with Qwen2-72B.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.