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Kimi K2 vs Qwen3-105B

Kimi K2 (2025) and Qwen3-105B (2025) are compact production models from Moonshot AI and Alibaba. Kimi K2 ships a 262K-token context window, while Qwen3-105B ships a 128k-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

Qwen3-105B is safer overall; choose Kimi K2 when long-context analysis matters.

Decision scorecard

Local evidence first
SignalKimi K2Qwen3-105B
Decision fitRAG, Agents, and Long contextRAG, Agents, and Long context
Context window262K128k
Cheapest output$2/1M tokens-
Provider routes3 tracked0 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose Kimi K2 when...
  • Kimi K2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Kimi K2 has broader tracked provider coverage for fallback and procurement flexibility.
  • Kimi K2 uniquely exposes Structured outputs in local model data.
  • Local decision data tags Kimi K2 for RAG, Agents, and Long context.
Choose Qwen3-105B when...
  • Qwen3-105B uniquely exposes Tool use in local model data.
  • Local decision data tags Qwen3-105B for RAG, Agents, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

Kimi K2

$900

Cheapest tracked route: AWS Bedrock

Qwen3-105B

Unavailable

No complete token price in local provider data

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

Switch friction

Kimi K2 -> Qwen3-105B
  • No overlapping tracked provider route is sourced for Kimi K2 and Qwen3-105B; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Structured outputs before moving production traffic.
  • Qwen3-105B adds Tool use in local capability data.
Qwen3-105B -> Kimi K2
  • No overlapping tracked provider route is sourced for Qwen3-105B and Kimi K2; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Tool use before moving production traffic.
  • Kimi K2 adds Structured outputs in local capability data.

Specs

Specification
Released2025-07-112025-12-15
Context window262K128k
Parameters1K105B
Architecture--
LicenseProprietaryOpen Source
Knowledge cutoff-2025-02

Pricing and availability

Pricing attributeKimi K2Qwen3-105B
Input price$0.5/1M tokens-
Output price$2/1M tokens-
Providers-

Capabilities

CapabilityKimi K2Qwen3-105B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingYesYes
Tool useNoYes
Structured outputsYesNo
Code executionNoNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on tool use: Qwen3-105B and structured outputs: Kimi K2. Both models share function calling, 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: Kimi K2 has $0.5/1M input tokens and Qwen3-105B has no token price sourced yet. Provider availability is 3 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Kimi K2 when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Qwen3-105B when provider fit 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Which has a larger context window, Kimi K2 or Qwen3-105B?

Kimi K2 supports 262K tokens, while Qwen3-105B supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Is Kimi K2 or Qwen3-105B open source?

Kimi K2 is listed under Proprietary. Qwen3-105B is listed under Open Source. 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 function calling, Kimi K2 or Qwen3-105B?

Both Kimi K2 and Qwen3-105B 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.

Which is better for tool use, Kimi K2 or Qwen3-105B?

Qwen3-105B has the clearer documented tool use signal in this comparison. If 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 structured outputs, Kimi K2 or Qwen3-105B?

Kimi K2 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 Kimi K2 and Qwen3-105B?

Kimi K2 is available on OpenRouter, AWS Bedrock, and GCP Vertex AI. Qwen3-105B 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: 2026-05-11. Data sourced from public model cards and provider documentation.