Qwen3.5 35B A3B vs Kimi K2 Instruct

At a Glance

Compare
Kimi K2 InstructMoonshot AI
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.14Deepinfra · Sep 23, 2026$0.57Openrouter · Aug 29, 2026
Output priceFrom · USD / 1M tokens$1.00Deepinfra · Sep 23, 2026$2.30Openrouter · Aug 29, 2026
Context windowMaximum documented tokens262K131K
Model facts checkedAug 28, 2026View model evidence →Aug 28, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldQwen3.5-35B-A3BKimi-K2-Instruct
DeveloperQwenMoonshot AI
FamilyQwen3 5 35b A3bKimi K2 Instruct
ModelQwen3.5-35B-A3BKimi-K2-Instruct
VersionQwen3.5-35B-A3BKimi-K2-Instruct
Lifecycleactiveactive
ReleasedUnknown2025-07-11
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText
Output modalitiesTextText
Context window262K131K
Total parameters36B1T
Active parameters3B32B
Licenseapache-2.0other
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Hugging Face (Standard), Openrouter (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, tools

Qwen3.5 35B A3B Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDQwen/Qwen3.5-35B-A3B

Kimi K2 Instruct Capabilities

chatgenerationtools
Serving providers2
Canonical IDmoonshotai/Kimi-K2-Instruct

Primary Evidence

Sources and Freshness

Questions

Qwen3.5 35B A3B vs Kimi K2 Instruct FAQs

Is Qwen3.5 35B A3B or Kimi K2 Instruct better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 35B A3B and Kimi K2 Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.5 35B A3B or Kimi K2 Instruct?+

Qwen3.5 35B A3B is $0.14 and Kimi K2 Instruct is $0.57 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric. Qwen3.5 35B A3B is $1.00 and Kimi K2 Instruct is $2.30 per million tokens, so Qwen3.5 35B A3B is cheaper on this metric.

Which has a larger context window, Qwen3.5 35B A3B or Kimi K2 Instruct?+

Qwen3.5 35B A3B has the larger sourced context window. Qwen3.5 35B A3B supports 262K and Kimi K2 Instruct supports 131K.

Which performs better in benchmarks, Qwen3.5 35B A3B or Kimi K2 Instruct?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Qwen3.5 35B A3B or Kimi K2 Instruct be self-hosted?+

Both models have the same recorded self-hosting status: supported. Qwen3.5 35B A3B is open weight; Kimi K2 Instruct is open weight.

Can Qwen3.5 35B A3B and Kimi K2 Instruct understand images?+

Qwen3.5 35B A3B is documented with image input; Kimi K2 Instruct is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Qwen3.5 35B A3B or Kimi K2 Instruct?+

Neither has a larger sourced maximum output. Qwen3.5 35B A3B is — and Kimi K2 Instruct is —.

Do Qwen3.5 35B A3B and Kimi K2 Instruct support reasoning and tool use?+

Qwen3.5 35B A3B: reasoning, tool calling, and image input. Kimi K2 Instruct: tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Qwen3.5 35B A3B or Kimi K2 Instruct?+

Qwen3.5 35B A3B has 4 sourced provider routes; Kimi K2 Instruct has 2, so Qwen3.5 35B A3B has broader tracked availability.

Which offers better value, Qwen3.5 35B A3B or Kimi K2 Instruct?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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