LLM Reference

Llama 3.2 11B Instruct vs Qwen3-Next-80B-A3B

Llama 3.2 11B Instruct (2025) and Qwen3-Next-80B-A3B (2025) are compact production models from AI at Meta and Alibaba. Llama 3.2 11B Instruct ships a 128K-token context window, while Qwen3-Next-80B-A3B ships a not-yet-sourced context window. On pricing, Qwen3-Next-80B-A3B costs $0.10/1M input tokens versus $0.20/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

Qwen3-Next-80B-A3B is ~105% cheaper at $0.10/1M; pay for Llama 3.2 11B Instruct only for provider fit.

Decision scorecard

Local evidence first
SignalLlama 3.2 11B InstructQwen3-Next-80B-A3B
Best forgeneral production evaluationprovider-routed production
Decision fitRAG, Long context, and ClassificationClassification and JSON / Tool use
Context window128K
Cheapest output$0.27/1M tokens$0.78/1M tokens
Provider routes1 tracked5 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Llama 3.2 11B Instruct when...
  • Llama 3.2 11B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Llama 3.2 11B Instruct has the lower cheapest tracked output price at $0.27/1M tokens.
  • Local decision data tags Llama 3.2 11B Instruct for RAG, Long context, and Classification.
Choose Qwen3-Next-80B-A3B when...
  • Qwen3-Next-80B-A3B has broader tracked provider coverage for fallback and procurement flexibility.
  • Local decision data tags Qwen3-Next-80B-A3B for Classification and JSON / Tool use.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Lower estimate Llama 3.2 11B Instruct

Llama 3.2 11B Instruct

$228

Cheapest tracked route/tier: AWS Bedrock

Qwen3-Next-80B-A3B

$273

Cheapest tracked route/tier: OpenRouter

Estimated monthly gap: $45.50. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

Llama 3.2 11B Instruct -> Qwen3-Next-80B-A3B
  • Provider overlap exists on AWS Bedrock; start route-level A/B tests there.
  • Qwen3-Next-80B-A3B is $0.51/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Qwen3-Next-80B-A3B -> Llama 3.2 11B Instruct
  • Provider overlap exists on AWS Bedrock; start route-level A/B tests there.
  • Llama 3.2 11B Instruct is $0.51/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.

Specs

Specification
Released2025-09-012025-12-01
Context window128K
Parameters11B80B (3B active)
Architecture--
LicenseProprietaryProprietary
Knowledge cutoff2023-12-

Pricing and availability

Pricing attributeLlama 3.2 11B InstructQwen3-Next-80B-A3B
Input price$0.20/1M tokens$0.10/1M tokens
Output price$0.27/1M tokens$0.78/1M tokens
Providers

Capabilities

CapabilityLlama 3.2 11B InstructQwen3-Next-80B-A3B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsYesYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

For cost, Llama 3.2 11B Instruct lists $0.20/1M input and $0.27/1M output tokens on the cheapest tracked provider, while Qwen3-Next-80B-A3B lists $0.10/1M input and $0.78/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 11B Instruct lower by about $0.08 per million blended tokens. Availability is 1 providers versus 5, so concentration risk also matters.

Choose Llama 3.2 11B Instruct when provider fit are central to the workload. Choose Qwen3-Next-80B-A3B when provider fit, lower input-token cost, 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.

FAQ

Which is cheaper, Llama 3.2 11B Instruct or Qwen3-Next-80B-A3B?

Qwen3-Next-80B-A3B is cheaper on tracked token pricing. Llama 3.2 11B Instruct costs $0.20/1M input and $0.27/1M output tokens. Qwen3-Next-80B-A3B costs $0.10/1M input and $0.78/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Llama 3.2 11B Instruct or Qwen3-Next-80B-A3B open source?

Llama 3.2 11B Instruct is listed under Proprietary. Qwen3-Next-80B-A3B is listed under Proprietary. 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 3.2 11B Instruct or Qwen3-Next-80B-A3B?

Both Llama 3.2 11B Instruct and Qwen3-Next-80B-A3B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Where can I run Llama 3.2 11B Instruct and Qwen3-Next-80B-A3B?

Llama 3.2 11B Instruct is available on AWS Bedrock. Qwen3-Next-80B-A3B is available on AWS Bedrock, OpenRouter, Venice AI, Novita AI, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Llama 3.2 11B Instruct over Qwen3-Next-80B-A3B?

Qwen3-Next-80B-A3B is ~105% cheaper at $0.10/1M; pay for Llama 3.2 11B Instruct only for provider fit. If your workload also depends on provider fit, start with Llama 3.2 11B Instruct; if it depends on provider fit, run the same evaluation with Qwen3-Next-80B-A3B.

Continue comparing

Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.