Llama 3.3 70B vs Qwen2.5-72B-Instruct
Llama 3.3 70B (2025) and Qwen2.5-72B-Instruct (2024) are compact production models from AI at Meta and Alibaba. Llama 3.3 70B ships a 8k-token context window, while Qwen2.5-72B-Instruct ships a 128k-token context window. On pricing, Qwen2.5-72B-Instruct costs $0.18/1M input tokens versus $0.90/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen2.5-72B-Instruct is ~400% cheaper at $0.18/1M; pay for Llama 3.3 70B only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Llama 3.3 70B | Qwen2.5-72B-Instruct |
|---|---|---|
| Best for | multimodal apps and tool-calling agents | provider-routed production |
| Decision fit | Agents, Vision, and Classification | Coding, RAG, and Long context |
| Context window | 8k | 128k |
| Cheapest output | $0.90/1M tokens | $0.54/1M tokens |
| Provider routes | 1 tracked | 7 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Llama 3.3 70B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Llama 3.3 70B for Agents, Vision, and Classification.
- Qwen2.5-72B-Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen2.5-72B-Instruct has the lower cheapest tracked output price at $0.54/1M tokens.
- Qwen2.5-72B-Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen2.5-72B-Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen2.5-72B-Instruct for Coding, RAG, 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
Qwen2.5-72B-Instruct
$279
Cheapest tracked route/tier: Chutes AI
Estimated monthly gap: $666. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Qwen2.5-72B-Instruct is $0.36/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
- Qwen2.5-72B-Instruct adds Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3.3 70B is $0.36/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Llama 3.3 70B adds Vision, Multimodal, and Function calling in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-09 | 2024-06-07 |
| Context window | 8k | 128k |
| Parameters | 70B | 72.7B |
| Architecture | decoder only | 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 | - |
Pricing and availability
| Pricing attribute | Llama 3.3 70B | Qwen2.5-72B-Instruct |
|---|---|---|
| Input price | $0.90/1M tokens | $0.18/1M tokens |
| Output price | $0.90/1M tokens | $0.54/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3.3 70B | Qwen2.5-72B-Instruct |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Llama 3.3 70B, multimodal input: Llama 3.3 70B, function calling: Llama 3.3 70B, tool use: Llama 3.3 70B, and structured outputs: Qwen2.5-72B-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.
For cost, Llama 3.3 70B lists $0.90/1M input and $0.90/1M output tokens on the cheapest tracked provider, while Qwen2.5-72B-Instruct lists $0.18/1M input and $0.54/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $0.61 per million blended tokens. Availability is 1 providers versus 7, so concentration risk also matters.
Choose Llama 3.3 70B when vision-heavy evaluation are central to the workload. Choose Qwen2.5-72B-Instruct when long-context analysis, larger context windows, 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.
FAQ
Which has a larger context window, Llama 3.3 70B or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct 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.
Which is cheaper, Llama 3.3 70B or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. Llama 3.3 70B costs $0.90/1M input and $0.90/1M output tokens. Qwen2.5-72B-Instruct costs $0.18/1M input and $0.54/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3.3 70B or Qwen2.5-72B-Instruct open source?
Llama 3.3 70B is listed under Llama 3 Community. Qwen2.5-72B-Instruct 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 Qwen2.5-72B-Instruct?
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 Qwen2.5-72B-Instruct?
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.
Where can I run Llama 3.3 70B and Qwen2.5-72B-Instruct?
Llama 3.3 70B is available on Fireworks AI. Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.