LLM Reference

Llama 3.2 11B Vision vs Qwen3.5-397B-A17B

Llama 3.2 11B Vision (2024) and Qwen3.5-397B-A17B (2026) are frontier reasoning models from AI at Meta and Alibaba. Llama 3.2 11B Vision ships a 128k-token context window, while Qwen3.5-397B-A17B ships a 262k-token context window. On MMLU PRO, Qwen3.5-397B-A17B leads by 41.4 pts. On pricing, Llama 3.2 11B Vision costs $0.20/1M input tokens versus $0.39/1M 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.

Llama 3.2 11B Vision is ~95% cheaper at $0.20/1M; pay for Qwen3.5-397B-A17B only for reasoning depth.

Decision scorecard

Local evidence first
SignalLlama 3.2 11B VisionQwen3.5-397B-A17B
Best formultimodal appsreasoning-heavy apps, multimodal apps, and tool-calling agents
Decision fitRAG, Long context, and VisionCoding, RAG, and Agents
Context window128k262k
Cheapest output$0.27/1M tokens$2.34/1M tokens
Provider routes1 tracked4 tracked
Shared benchmarks2 sharedMMLU PRO leader

Decision tradeoffs

Choose Llama 3.2 11B Vision when...
  • Llama 3.2 11B Vision has the lower cheapest tracked output price at $0.27/1M tokens.
  • Local decision data tags Llama 3.2 11B Vision for RAG, Long context, and Vision.
Choose Qwen3.5-397B-A17B when...
  • Qwen3.5-397B-A17B holds a shared-benchmark lead on MMLU PRO, ahead by 41.4 points.
  • Qwen3.5-397B-A17B has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Qwen3.5-397B-A17B has broader tracked provider coverage for fallback and route flexibility.
  • Qwen3.5-397B-A17B uniquely exposes Multimodal, Reasoning, and JSON / Tool use in local model data.
  • Local decision data tags Qwen3.5-397B-A17B for Coding, RAG, and Agents.

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 Vision

Llama 3.2 11B Vision

$228

Cheapest tracked route/tier: AWS Bedrock

Qwen3.5-397B-A17B

$897

Cheapest tracked route/tier: OpenRouter

Estimated monthly gap: $670. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

Llama 3.2 11B Vision -> Qwen3.5-397B-A17B
  • No overlapping tracked provider route is sourced for Llama 3.2 11B Vision and Qwen3.5-397B-A17B; plan for SDK, billing, or endpoint changes.
  • Qwen3.5-397B-A17B is $2.07/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • Qwen3.5-397B-A17B adds Multimodal, Reasoning, and JSON / Tool use in local capability data.
Qwen3.5-397B-A17B -> Llama 3.2 11B Vision
  • No overlapping tracked provider route is sourced for Qwen3.5-397B-A17B and Llama 3.2 11B Vision; plan for SDK, billing, or endpoint changes.
  • Llama 3.2 11B Vision is $2.07/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Multimodal, Reasoning, and JSON / Tool use before moving production traffic.

Specs

Specification
Released2024-09-252026-02-16
Context window128k262k
Parameters10.6B397B
ArchitectureDecoder OnlyMixture of Experts
LicenseLlama 3 CommunityApache 2.0OSI-approved
OpennessOpen weightsOpen source
WeightsUnknownAvailable
CodeUnknownUnknown
Commercial useCommercial use: conditionalCommercial use: permitted
Knowledge cutoff2024-03-

Pricing and availability

Pricing attributeLlama 3.2 11B VisionQwen3.5-397B-A17B
Input price$0.20/1M tokens$0.39/1M tokens
Output price$0.27/1M tokens$2.34/1M tokens
Providers

Capabilities

CapabilityLlama 3.2 11B VisionQwen3.5-397B-A17B
VisionYesYes
MultimodalNoYes
ReasoningNoYes
JSON / Tool useNoYes
Structured outputsYesYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

BenchmarkLlama 3.2 11B VisionQwen3.5-397B-A17B
MMLU PRO46.487.8
Massive Multi-discipline Multimodal Understanding50.785.0

Continue comparing

Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.