LLM Reference

Llama 3.3 70B vs Qwen2.5-72B

Llama 3.3 70B (2025) and Qwen2.5-72B (2025) are compact production models from AI at Meta and Alibaba. Llama 3.3 70B ships a 8k-token context window, while Qwen2.5-72B ships a 128k-token context window. On MMLU PRO, Qwen2.5-72B leads by 0.7 pts. 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 fits 16x more tokens; pick it for long-context work and Llama 3.3 70B for tighter calls.

Decision scorecard

Local evidence first
SignalLlama 3.3 70BQwen2.5-72B
Best formultimodal apps and tool-calling agentstool-calling agents
Decision fitAgents, Vision, and ClassificationRAG, Agents, and Long context
Context window8k128k
Cheapest output$0.90/1M tokens-
Provider routes1 tracked0 tracked
Shared benchmarks1 rowsMMLU PRO leader

Decision tradeoffs

Choose Llama 3.3 70B when...
  • 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.
Choose Qwen2.5-72B when...
  • Qwen2.5-72B holds a shared-benchmark lead on MMLU PRO, ahead by 0.7 points.
  • Qwen2.5-72B has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Local decision data tags Qwen2.5-72B 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

Qwen2.5-72B

Unavailable

No complete token price in local provider data

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Llama 3.3 70B -> Qwen2.5-72B
  • No overlapping tracked provider route is sourced for Llama 3.3 70B and Qwen2.5-72B; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision and Multimodal before moving production traffic.
Qwen2.5-72B -> Llama 3.3 70B
  • No overlapping tracked provider route is sourced for Qwen2.5-72B 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
Released2025-12-092025-10-10
Context window8k128k
Parameters70B72B
Architecturedecoder only-
LicenseLlama 3 CommunityApache 2.0(OSI)
OpennessOpen weightsOpen source
Commercial useCommercial use with conditionsCommercial use allowed
Knowledge cutoff2024-122024-09

Pricing and availability

Pricing attributeLlama 3.3 70BQwen2.5-72B
Input price$0.90/1M tokens-
Output price$0.90/1M tokens-
Providers-

Capabilities

CapabilityLlama 3.3 70BQwen2.5-72B
VisionYesNo
MultimodalYesNo
ReasoningNoNo
Function callingYesYes
Tool useYesYes
Structured outputsNoNo
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

BenchmarkLlama 3.3 70BQwen2.5-72B
MMLU PRO71.372.0

Deep dive

On shared benchmark coverage, MMLU PRO has Llama 3.3 70B at 71.3 and Qwen2.5-72B at 72, with Qwen2.5-72B ahead by 0.7 points. The largest visible gap is 0.7 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: 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 Qwen2.5-72B 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 Qwen2.5-72B 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 3.3 70B or Qwen2.5-72B?

Qwen2.5-72B 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 Qwen2.5-72B open source?

Llama 3.3 70B is listed under Llama 3 Community. Qwen2.5-72B 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 vision, Llama 3.3 70B or Qwen2.5-72B?

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?

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 Qwen2.5-72B?

Both Llama 3.3 70B and Qwen2.5-72B 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 Qwen2.5-72B?

Llama 3.3 70B is available on Fireworks AI. Qwen2.5-72B 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.