Llama 3.3 70B Instruct vs Qwen2.5-72B

Name: Llama 3.3 70B Instruct
Author: AI at Meta

Llama 3.3 70B Instruct (2024) and Qwen2.5-72B (2025) are compact production models from AI at Meta and Alibaba. Both ship a 128K-token context window. On MMLU PRO, Qwen2.5-72B leads by 3.1 pts. 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.

Qwen2.5-72B is safer overall; choose Llama 3.3 70B Instruct when provider fit matters.

Decision scorecard

Local evidence first

Signal	Llama 3.3 70B Instruct	Qwen2.5-72B	How to read it
Best for	provider-routed production	tool-calling agents	Use-case synthesis from product type, capability flags, context, and provider data.
Decision fit	RAG, Long context, and Classification	RAG, Agents, and Long context	Primary workload tags from local decision data.
Context window	128K	128K	Higher is better when prompts, retrieval chunks, or transcripts are large.
Cheapest output	$0.40/1M tokens	-	Cheapest tracked provider route; verify your exact region and tier.
Provider routes	13 tracked	0 tracked	Broader coverage can reduce vendor lock-in and fallback risk.
Shared benchmarks	1 shared	MMLU PRO leader	Visible benchmark lead is 3.1 points on MMLU PRO.

Decision tradeoffs

Choose Llama 3.3 70B Instruct when...

Llama 3.3 70B Instruct has broader tracked provider coverage for fallback and route flexibility.
Llama 3.3 70B Instruct uniquely exposes Structured outputs in local model data.
Local decision data tags Llama 3.3 70B Instruct for RAG, Long context, and Classification.

Choose Qwen2.5-72B when...

Qwen2.5-72B holds a shared-benchmark lead on MMLU PRO, ahead by 3.1 points.
Qwen2.5-72B uniquely exposes JSON / Tool use in local model data.
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.

Requests / monthInput tokens / requestOutput tokens / request

Llama 3.3 70B Instruct

$208

Cheapest tracked route/tier: Novita 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 Instruct -> Qwen2.5-72B

No overlapping tracked provider route is sourced for Llama 3.3 70B Instruct and Qwen2.5-72B; plan for SDK, billing, or endpoint changes.
Check replacement coverage for Structured outputs before moving production traffic.
Qwen2.5-72B adds JSON / Tool use in local capability data.

Qwen2.5-72B -> Llama 3.3 70B Instruct

No overlapping tracked provider route is sourced for Qwen2.5-72B and Llama 3.3 70B Instruct; plan for SDK, billing, or endpoint changes.
Check replacement coverage for JSON / Tool use before moving production traffic.
Llama 3.3 70B Instruct adds Structured outputs in local capability data.

Specs

Specification	Llama 3.3 70B Instruct AI at Meta	Qwen2.5-72B Alibaba
Released	2024-12-06	2025-10-10
Context window	128K	128K
Parameters	70B	72B
Architecture	Decoder Only	-
License	Llama 3.3 Community	Apache 2.0OSI-approved
Openness	Open weights	Open source
Weights	Available	Available
Code	Unknown	Unknown
Commercial use	Restricted	Permitted
Knowledge cutoff	2023-12	2024-09

Pricing and availability

Pricing attribute	Llama 3.3 70B Instruct	Qwen2.5-72B
Input price	$0.14/1M tokens	-
Output price	$0.40/1M tokens	-
Providers	Cloudflare Workers AI Fireworks AI NVIDIA NIM GroqCloud Together AI AWS Bedrock	-

Capabilities

Capability	Llama 3.3 70B Instruct	Qwen2.5-72B
Vision	No	No
Multimodal	No	No
Reasoning	No	No
JSON / Tool use	No	Yes
Structured outputs	Yes	No
Code execution	No	No
IDE integration	No	No
Computer use	No	No
Parallel agents	No	No

Benchmarks

Benchmark	Llama 3.3 70B Instruct	Qwen2.5-72B
MMLU PRO	68.9	72.0

Deep dive

On shared benchmark coverage, MMLU PRO has Llama 3.3 70B Instruct at 68.9 and Qwen2.5-72B at 72, with Qwen2.5-72B ahead by 3.1 points. The largest visible gap is 3.1 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 JSON / Tool use: Qwen2.5-72B, JSON / Tool use: Qwen2.5-72B, and structured outputs: Llama 3.3 70B 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 3.3 70B Instruct has $0.14/1M input tokens and Qwen2.5-72B has no token price sourced yet. Provider availability is 13 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 Instruct when provider fit and broader provider choice are central to the workload. Choose Qwen2.5-72B when provider fit 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 Instruct or Qwen2.5-72B?

Llama 3.3 70B Instruct supports 128K tokens, while Qwen2.5-72B supports 128K 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 Instruct or Qwen2.5-72B open source?

Llama 3.3 70B Instruct is listed under Llama 3.3 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 JSON / Tool use, Llama 3.3 70B Instruct or Qwen2.5-72B?

Qwen2.5-72B has the clearer documented JSON / Tool use signal in this comparison. If JSON / Tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for JSON / Tool use, Llama 3.3 70B Instruct or Qwen2.5-72B?

Which is better for structured outputs, Llama 3.3 70B Instruct or Qwen2.5-72B?

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

Llama 3.3 70B Instruct is available on Cloudflare Workers AI, Fireworks AI, NVIDIA NIM, GroqCloud, and Together 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

Model pages

Labs and families

Related comparisons

Popular comparisons for Llama 3.3 70B Instruct

Popular comparisons for Qwen2.5-72B

Last reviewed: 2026-10-17. Data sourced from public model cards and provider documentation.

Both models

Llama 3.3 70B Instruct Qwen2.5-72B