Qwen3 vs Kimi K2.5: Which Open-Source AI Model Should You Use in 2026?

Quick Comparison

Before we get into the details, let's quickly set the stage and explain the differences and key features of each model.

‍

What is Qwen 3?

Qwen 3 is the latest generation of large language models from Alibaba Cloud’s Qwen team. It was released on April 28, 2025 — which, in AI terms, already feels like a while ago. Even so, the stronger models in the Qwen 3 family are still among the top-performing AI systems in the world.

‍

That’s partly because Qwen 3 isn’t a single model — it’s an entire lineup. The family ranges from a lightweight 0.6B parameter model you can run on a phone to a massive 235B-parameter Mixture-of-Experts (MoE) model that rivals proprietary frontier systems.

‍

Qwen 3 can also switch seamlessly between “thinking” and “non-thinking” modes, similar to GPT-5.2 and Claude Sonnet 4.6. That gives it flexibility: you can prioritize deeper reasoning when needed or faster responses for simpler tasks.

‍

Another standout feature is its 256K token context window, expandable up to 1 million tokens. Overall, Qwen 3 is one of the most broadly capable open-source model families currently available.

‍

What is Kimi K2.5?

Kimi K2.5 is Moonshot AI’s latest multimodal model — and one of the most capable LLMs on the market.

‍

It’s built on a 1-trillion-parameter Mixture-of-Experts (MoE) architecture, though only 32 billion parameters are activated per request. In practical terms, that means the model has enormous raw capacity, but it only “turns on” the parts relevant to the task at hand. This keeps inference costs lower and response times faster without sacrificing quality.

‍

Its headline feature is Agent Swarm. Kimi K2.5 can break down complex tasks and delegate them to up to 100 parallel AI sub-agents, each operating independently with access to tools. 

‍

Agent Swarm can dramatically speed up work — though it’s mainly relevant when you deploy the model locally — or use it via something like OpenClaw.

‍

Qwen 3 vs Kimi K2.5 Comparison Table

Here’s how the two models compare at a glance:

‍

‍

Now that we understand the capabilities of each model, let’s see what the benchmarks tell us.

‍

Qwen 3 vs Kimi K2.5 Benchmarks

Benchmarks give us a concrete way to compare raw performance. For this comparison, we're using the latest available versions

‍

Qwen3-235B-A22B-Thinking-2507 (updated July 2025) and Kimi K2.5 (January 2026). All scores are self-reported or from third-party analysis, so take them with a grain of salt — real-world performance often depends more on your specific task and prompting than on aggregate benchmark scores.

‍

Math & Reasoning

These benchmarks test the models' ability to solve hard math problems and reason through complex scientific questions.

‍

‍

Both models are excellent for math tasks and can easily help with homework or solve graduate-level problems. That said, the Kimi K2.5 is more accurate.

‍

Winner: Kimi K2.5

‍

Coding

‍

‍

When it comes to coding, Kimi K2.5 clearly outperforms Qwen 3. In fact, it comes close to last-generation proprietary coding models like Claude Opus 4.5, which scored 80.9%.

‍

That said, the newer Qwen 3.5 turns the tables — it surpasses Kimi with an impressive 91.3% score.

‍

Winner: Kimi K2.5 — at least when comparing like-for-like vs Qwen3.

‍

Qwen 3 vs Kimi K2.5 Features

This is where things get interesting.

‍

Features Both Models Have

Thinking modes. Both models offer thinking and non-thinking modes. Qwen 3 calls them "thinking" and "non-thinking." Kimi K2.5 calls them "Thinking" and "Instant." Both let the model decide how much reasoning effort to invest.

‍

Tool use. Both models can integrate with external tools and APIs. Qwen 3 achieves leading performance among open-source models on agent-based tasks. Kimi K2.5 takes this further with multi-tool orchestration.

‍

Open weights. Both models are available for download and local deployment. Qwen 3 uses Apache 2.0 (very permissive). Kimi K2.5 uses Modified MIT (free for most commercial use, attribution required above 100M MAU or $20M monthly revenue).

‍

‍

Features Only Qwen 3 Has

Many model sizes. Eight, in fact, 0.6B to 235B parameters.

‍

Massive ecosystem. Qwen 3 is supported by virtually every inference framework: vLLM, SGLang, llama.cpp, Ollama, LM Studio, mlx-lm, and more.

‍

Ultra-light local deployment. The 0.6B and 1.7B models can easily run on your PC or MacBook — you only need  2GB of RAM. 

‍

There’s also an upgrade path in the form of Qwen3.5, which came out in February 2026 — this model adds native vision, and upgrades performance across every area.

‍

Features Only Kimi K2.5 Has

Agent Swarm. K2.5 can create and coordinate up to 100 sub-agents at the same time, which cuts execution time by up to 4.5x on complex tasks. No other open-source model offers anything like this.

‍

Native multimodal vision. Trained on visual and text data from the start, K2.5 excels at coding from visual specifications, like turning UI mockups into working front-end code.

‍

Office productivity agent. Kimi K2.5 can create and edit Word documents, Excel spreadsheets with formulas and work with PDF files, making it a more powerful AI assistant.

‍

Video support. Kimi K2.5 can understand what it sees in videos.

‍

Kimi Code CLI. A dedicated command-line tool optimized for agentic coding with K2.5.

‍

Qwen 3 vs Kimi K2.5 Pricing

Both models are open-weight, which means you can run them for free on your own hardware. But for API access, here's how the costs compare:

‍

API pricing

‍

‍

A few things jump out here:

‍

Qwen3-235B-A22B Instruct ($0.70/$2.80) and Kimi K2.5 Instant ($0.60/$2.50) are in a similar ballpark, with Kimi being slightly cheaper on both input and output.

‍

For reasoning tasks, Qwen3-235B-A22B Thinking charges $8.40 per million output tokens — more than 3x what Kimi K2.5 charges for Thinking mode ($2.50).

‍

This is because Qwen 3's thinking mode generates long reasoning chains that rack up output tokens, and Alibaba charges more per token for them. Kimi K2.5 charges the same rate regardless of mode.‍

‍

Self-Hosting Costs

This is where Qwen 3 has a clear advantage. With models as small as 0.6B, you can run Qwen 3 on consumer hardware for zero cost. Kimi K2.5, with its 1 trillion parameter MoE architecture, requires serious GPU infrastructure for local deployment.

‍

Want to try both without worrying about infrastructure? You can access both models on Overchat AI by creating a free account here.

‍

Winner: Qwen 3 for self-hosting, Kimi K2.5 for API

‍

Qwen 3 vs Kimi K2.5: Real-World Comparison

Benchmarks and feature lists only tell part of the story. Here's how both models performed on practical tasks. For fairness, we ran both models in Overchat AI.

‍

Writing

For creative writing, we tested each model's ability to produce engaging, original prose. We gave each model the following prompt:

‍

Creative writing prompt: Write the opening paragraph (150-200 words) of a thriller short story about a deep-sea researcher who discovers that an AI running a remote underwater station has started making decisions on its own. The tone should be tense and claustrophobic. Focus on the environment, the character's growing unease, and small details that feel wrong.

‍

The results:

‍

‍

I wasn’t expecting a major difference here — but there was one. Kimi K2.5’s writing feels noticeably less formulaic. It relies less on obvious “AI-isms” and reads more like an actual narrative unfolding, rather than a collection of polished but predictable phrases.

‍

By contrast, Qwen tends to overuse the same “fancy” sentence structures without a clear sense of why they work. The result often feels generic and overly templated.

‍

If most of your work revolves around writing and long-form text, Kimi K2.5 is the safer bet between the two.

‍

Math

We gave the models a multi-step optimization problem from a university-level calculus course (Calculus II). It tests understanding of integration, surface area formulas, and practical application of constrained optimization.

‍

The problem:

‍

A spherical storage tank is to be constructed to hold exactly 500 cubic meters of gas. The tank will be coated with two different materials: the upper hemisphere uses a heat-resistant coating that costs $12 per square meter, and the lower hemisphere uses a standard coating that costs $8 per square meter. Find the radius that minimizes the total coating cost. Show your complete work including:

‍

1. Setting up the cost function in terms of the radius

2. Expressing the surface area of each hemisphere

3. Taking derivatives

4. Solving for the critical point

5. Confirming the result is a minimum

‍

The results:

‍

‍

Both models solved the task correctly, so it’s a tie.

‍

Bottom Line

Here's where the two models stack up after our full comparison:

‍

Qwen 3 wins on:

‍

• Model variety and local deployment flexibility
• Ecosystem and community support
• Language coverage (119 languages)
• Self-hosting cost efficiency

‍

Kimi K2.5 wins on:

‍

• Agentic workflows
• Understanding images and videos
• Working with office documents
• API pricing
• Creative writing

‍

Draws:

‍

• Both nave thinking/reasoning modes
• Both can use tools
• Both use open-weight licensing
• Math performance

‍

That said, there’s an important nuance here: these models aren’t really competing head-to-head.

‍

Qwen 3 is the obvious choice if you need something you can run locally. Kimi K2.5, on the other hand, feels more like a “let’s build the best model we possibly can” project — less about efficiency and more about pushing the ceiling of what’s achievable. It’s almost the Crysis of AI: designed to show what’s possible at the highest level.

‍

So in the end, it really comes down to your specific needs.

‍

And here’s the good news — you don’t actually have to choose. You can access both Qwen 3 and Kimi K2.5 on Overchat AI right here:

‍

• Chat with Qwen 3
• Chat with Kimi K2.5