Kimi K2.5 Review: Moonshot AI's Open-Weight Agentic Model Tested
Moonshot AI dropped Kimi K2.5 in late January — a trillion-parameter open-weight model with a feature no other model has: Agent Swarm, which orchestrates up to 100 parallel sub-agents on a single task. We spent three weeks running it through coding, research, and document analysis workflows to see if the benchmarks translate to real utility.
TL;DR — Key Takeaways:
- • Agent Swarm is genuinely novel — coordinating up to 100 sub-agents in parallel for research and complex tasks, delivering roughly 4.5x speed improvements over sequential approaches
- • Vision-native architecture sets it apart — trained on 15 trillion mixed visual-text tokens, not an adapter bolted onto a text model. UI-to-code and visual debugging work noticeably well.
- • API pricing is absurdly cheap — $0.60 per million input tokens makes it roughly 25x cheaper than Claude Opus on input and 4x cheaper than GPT
- • Open weights with a practical license — self-host without restrictions unless you hit 100M MAU or $20M monthly revenue
What Is Kimi K2.5?
Kimi K2.5 is the latest model from Moonshot AI, a Beijing-based AI lab backed by Alibaba. Released January 27, 2026, it's a Mixture-of-Experts (MoE) model with around 1 trillion total parameters, but it only activates about 32 billion per request — keeping inference costs low despite the massive parameter count.
What makes K2.5 distinctive isn't raw reasoning power (though it has plenty). It's the architecture: 61 layers containing 384 experts with sparse 8-expert activation per token, natively multimodal training on approximately 15 trillion mixed vision-text tokens, and a novel Agent Swarm system that can spin up to 100 parallel sub-agents for complex tasks.
The model operates in four modes — Instant, Thinking, Agent, and Agent Swarm — each with different speed-accuracy tradeoffs. The context window sits at 256K tokens, roughly 500 pages of text. And the whole thing is open-weight under a modified MIT license, meaning you can self-host it.
At a Glance
Genuinely Impressive:
- • Agent Swarm — up to 100 parallel sub-agents, 4.5x speed boost
- • Vision-native — not bolted-on, trained end-to-end on visual+text
- • $0.60/MTok input — roughly 25x cheaper than Claude Opus
- • Open weights with practical commercial license
- • AIME 2025: 96.1%, SWE-Bench: 76.8%
Where It Falls Short:
- • 256K context window — smaller than Gemini 1M or Claude 200K beta
- • Output is verbose — generates ~6x more tokens than average models
- • Slower output speed (around 45 tok/s) than top competitors
- • SWE-Bench trails Claude's 80.9% on pure coding
- • English writing quality behind Claude and GPT
How We Tested
We evaluated Kimi K2.5 over three weeks using the API (Thinking and Agent Swarm modes) and the free chat interface at kimi.com. All comparisons with Claude, GPT, and Gemini used identical prompts. We cross-referenced our results with independent benchmarks from Artificial Analysis, the Codecademy guide, and community evaluations on Hugging Face.
Coding Tasks (8 projects)
React/Next.js component generation, Python data pipelines, multi-file refactoring in TypeScript, and bug hunting in Go. Tested in both Thinking and Agent modes.
Agent Swarm Evaluation (5 tasks)
Multi-document research synthesis, competitive analysis across 20+ company pages, and parallel code review across a monorepo. Compared Agent Swarm timing vs sequential Agent mode.
Vision and Multimodal (6 tests)
UI mockup to code conversion, chart analysis from screenshots, video understanding from product demos. Compared directly against GPT-4o and Gemini 2.5 Pro on identical inputs.
Third-Party Benchmark Verification
Cross-referenced Moonshot's published scores against Artificial Analysis Intelligence Index (score: 47, rank #2 of 66 models), LMSYS evaluations, and community reports.
API testing used standard pricing with no credits or partnerships from Moonshot AI. Token costs reported are actual billed amounts.
Key Features
K2.5 has a lot going on technically, but four capabilities actually matter in daily use.
What Sets It Apart
Four Operating Modes
Instant mode responds in 3–8 seconds for quick tasks. Thinking mode engages chain-of-thought reasoning (temperature 1.0) with visible reasoning traces. Agent mode handles multi-step workflows with 200–300 sequential tool calls. Agent Swarm coordinates up to 100 parallel sub-agents.
No other model offers this kind of modal flexibility. You pick the speed-accuracy tradeoff per task rather than being locked into one behavior.
Native Multimodal Architecture
Unlike models that bolt vision onto a text backbone via adapters, K2.5 was trained end-to-end on 15 trillion mixed visual-text tokens. This means images and text share the same representation space, which shows in practice: UI mockup-to-code conversion is noticeably more accurate than adapter-based approaches.
It handles text, images, and video input. Output is text-only — no image generation.
Open Weights with Practical License
The full model weights are on Hugging Face under a modified MIT license. Commercial use requires attribution only above 100M monthly active users or $20M monthly revenue. For the vast majority of companies and developers, that means effectively unrestricted use. Native INT4 quantization from the training phase provides about 2x speed improvements without degrading accuracy.
Mixture-of-Experts Efficiency
The 1 trillion total parameters sound intimidating, but only 32 billion activate per request thanks to the MoE architecture (384 experts, 8 active per token). This means you get near-frontier intelligence at a fraction of the compute cost — which is why the API pricing can be so aggressive.
Agent Swarm: The Headline Feature
Agent Swarm is the capability that has no direct equivalent in competing models. Instead of processing a complex task step by step, K2.5 decomposes it into subtasks and spawns specialized sub-agents — up to 100 of them — that work simultaneously.
On BrowseComp, a benchmark that tests web research capabilities, Agent Swarm pushed K2.5's score from 74.9% (single agent) to 78.4%. The practical implication: tasks that took 12–15 minutes in sequential Agent mode completed in under 4 minutes with Agent Swarm. Moonshot reports around 4.5x execution time reduction on parallelizable tasks, and our testing roughly confirmed that number on research-heavy workloads.
Where it shines: competitive analysis (spawn agents to investigate each competitor simultaneously), multi-document research synthesis, and large codebase review where different modules can be analyzed in parallel. Where it doesn't help: strictly sequential tasks like step-by-step debugging or linear document editing where each step depends on the previous one.
Agent Swarm vs Sequential Agent: Our Tests
| Task | Sequential Agent | Agent Swarm | Speedup |
|---|---|---|---|
| 20-company competitive analysis | ~14 min | ~3.5 min | 4x |
| 10-paper research synthesis | ~11 min | ~2.8 min | 3.9x |
| Monorepo code review (8 modules) | ~9 min | ~2.2 min | 4.1x |
| Sequential debugging (single file) | ~4 min | ~3.8 min | 1.05x |
Agent Swarm delivers massive speedups on parallelizable tasks but provides negligible benefit on inherently sequential work.
Coding Performance
K2.5 scores 76.8% on SWE-Bench Verified and 85.0% on LiveCodeBench. Those are strong numbers — not quite Claude Opus 4.5's 80.9% on SWE-Bench, but ahead of most open-weight alternatives and competitive with GPT-5.2.
In our testing, K2.5's coding strength is front-end work. Moonshot specifically optimized for UI generation, and it shows. Given a Figma screenshot, K2.5 in Thinking mode produced a React component with accurate layout, proper responsive breakpoints, and reasonable Tailwind classes on the first attempt. Claude matched the layout accuracy but took longer. GPT-4o missed some spacing details.
For backend and systems-level code, K2.5 is capable but not class-leading. A Python ETL pipeline came out clean and functional. A Go concurrency bug was found in Thinking mode but missed in Instant mode. Claude consistently caught more subtle bugs without needing to switch modes.
The vision-grounded coding capability is the real differentiator. If your workflow involves converting mockups, wireframes, or screenshots into code, K2.5 handles that pipeline more naturally than any model we've tested. It sees the image and reasons about code simultaneously rather than describing the image first and then generating code — a subtle but meaningful difference in output quality.
Kimi K2.5 vs Claude vs GPT vs Gemini
Four frontier-class models, four different strengths. Here's how they stack up on the dimensions that actually matter for daily work.
Where Each Model Wins
Kimi K2.5 — Agentic Tasks, Vision, and Budget
Agent Swarm is unmatched for parallelizable research and analysis. Vision-native architecture produces better UI-to-code results. And at $0.60/MTok input, running high-volume workloads costs a fraction of alternatives. The open-weight license seals it for teams that need to self-host.
Claude Opus 4.5 — Code Quality and Writing
Still the strongest on SWE-Bench (80.9%) and produces the most natural, nuanced prose. For a detailed comparison of Claude's coding capabilities, see our Claude Opus review. The developer tooling ecosystem (Claude Code, Agent Teams) is more mature.
GPT-5.2 — Pure Reasoning and Consumer Ecosystem
Leads on abstract reasoning benchmarks and has the most complete consumer product (plugins, voice, image generation, GPT Store). Still the default recommendation for non-technical users who want one AI tool.
Gemini 2.5 Pro — Long Context and Google Integration
The 1M token context window is 4x larger than K2.5's 256K. For teams processing massive documents or entire codebases in one shot, Gemini remains the better choice. See our Gemini 2.5 Pro review for the full breakdown.
Where Kimi K2.5 Falls Short
K2.5 is impressive on paper, but three weeks of daily use surfaced real limitations that the benchmarks don't capture.
Extreme Verbosity
Artificial Analysis flagged K2.5 as generating around 89 million tokens during their evaluation — roughly 6x the median model's output volume. In practice, this means responses are consistently longer than necessary. Ask for a three-paragraph summary, and you'll often get six. The information density is lower than Claude or GPT outputs.
This also inflates API costs since you're billed for output tokens you didn't want. The verbose output at $2.50–$3.00/MTok erodes the input cost advantage.
Slower Output Speed
At around 45 tokens per second, K2.5 ranks #31 out of 66 models on Artificial Analysis speed benchmarks. Claude and GPT are noticeably snappier for interactive use. The time-to-first-token of roughly 1.2 seconds is acceptable, but combined with verbose outputs, the total wait time for a complete response can feel long. Agent Swarm partially compensates by parallelizing, but individual responses still feel sluggish.
English Writing Quality
As a model from a Chinese AI lab, K2.5's strongest natural language performance is in Mandarin. English output is functional and accurate but often reads as translated — slightly formal, occasionally awkward phrasing, less idiomatic than Claude or GPT. For technical documentation and code comments this is fine. For marketing copy, blog posts, or any customer-facing content, you'll want to edit or use a different model.
256K Context Window Isn't Enough for Some Workflows
While 256K tokens covers most tasks comfortably, teams working with large monorepos or multi-hundred-page documents will hit the ceiling. Gemini's 1M context is 4x larger. Claude's 200K is close, and its 1M beta surpasses K2.5. The Agent Swarm can partially compensate by splitting large tasks across agents, but that adds complexity and doesn't solve single-prompt context needs.
Pricing and Access
This is where K2.5 makes its strongest economic argument. The API pricing is aggressively low, and the open-weight release means you can eliminate API costs entirely by self-hosting.
Access Options
| Access Method | Cost | Best For |
|---|---|---|
| kimi.com (free tier) | $0 | Testing, casual use, all four modes available |
| Moonshot API | $0.60 / $2.50–$3.00 per MTok | Production apps, high-volume workloads |
| Self-hosted (Hugging Face) | Infrastructure only | Teams needing full control, data sovereignty |
| Kimi Code (CLI) | Free | Terminal-based coding workflows |
To put the API pricing in perspective: running 10 million input tokens through K2.5 costs $6. The same volume through Claude Opus 4.5 costs $150. Through GPT-5.2, roughly $25. Through Gemini 2.5 Pro, about $12.50. If you're building an application that processes large volumes of text or runs frequent agent tasks, K2.5's pricing changes the economics fundamentally.
The caveat: K2.5's verbose output means you consume more output tokens than competing models for equivalent tasks. At $2.50–$3.00 per million output tokens, the verbosity tax narrows the cost gap. We estimate real-world effective costs are roughly 3–5x cheaper than Claude rather than the headline 25x, once you account for the additional output tokens.
Frequently Asked Questions
Is Kimi K2.5 free to use?
Yes. The chat interface at kimi.com offers free access with usage limits across all four modes (Instant, Thinking, Agent, and Agent Swarm). The model weights are also freely available on Hugging Face for self-hosting. API access through Moonshot costs $0.60 per million input tokens and around $2.50–$3.00 per million output tokens — making it one of the cheapest frontier-class APIs available.
How does Kimi K2.5 compare to ChatGPT?
K2.5 outperforms GPT-5.2 on vision benchmarks (MMMU Pro 78.5%), agentic tasks (BrowseComp 74.9%), and math (AIME 2025: 96.1% vs GPT's ~88%). GPT-5.2 maintains advantages in abstract reasoning, consumer features (plugins, voice, image generation), and English writing quality. K2.5 is dramatically cheaper on API pricing. The Agent Swarm capability has no GPT equivalent.
What is Agent Swarm in Kimi K2.5?
Agent Swarm orchestrates up to 100 specialized sub-agents working in parallel. Instead of tackling a complex task step by step, K2.5 decomposes it into subtasks and assigns each to a dedicated agent. This achieves roughly 4.5x faster execution on parallelizable work like multi-document research, competitive analysis, and codebase review. It doesn't help on inherently sequential tasks like step-by-step debugging.
What is the context window for Kimi K2.5?
256K tokens, which handles roughly 500 pages of standard text. This is larger than GPT-4o's 128K and comparable to Claude's 200K standard window. Gemini 2.5 Pro's 1M token context is significantly larger. For most tasks — coding, document analysis, typical research — 256K is sufficient. Large monorepos or multi-hundred-page documents may require Agent Swarm to split the work.
Can I self-host Kimi K2.5?
Yes. The model weights are available on Hugging Face under a modified MIT license. Commercial use is unrestricted below 100M monthly active users or $20M monthly revenue (attribution required above those thresholds). Deploy with vLLM, SGLang, or KTransformers. The INT4 quantization trained into the model provides 2x speed improvements without accuracy loss, making self-hosting more feasible than raw parameter counts suggest.
How much does Kimi K2.5 cost?
API pricing is $0.60 per million input tokens and $2.50–$3.00 per million output tokens. At a blended 3:1 ratio, effective cost is around $1.20 per million tokens. The free tier at kimi.com covers casual use. Self-hosting eliminates API costs entirely but requires significant GPU infrastructure for the 1T-parameter model. In practice, the verbose output inflates costs above the headline rates, but K2.5 remains 3–5x cheaper than alternatives for equivalent tasks.
Final Verdict
Kimi K2.5 is the most interesting open-weight model release since Llama 3. Not because it's the best at everything — it isn't. Claude writes better. GPT reasons more abstractly. Gemini handles longer contexts. But K2.5 introduces a genuinely novel capability in Agent Swarm that no competitor matches, packages it with competitive benchmark scores, and releases it all under a practical open license at a fraction of the price.
The math reasoning scores are remarkable — 96.1% on AIME 2025 puts it at the top alongside dedicated reasoning models. The vision-native architecture makes UI-to-code workflows feel like a step forward rather than an incremental improvement. And the $0.60/MTok input pricing opens up use cases that simply aren't economical with Claude or GPT at 10–25x the cost.
The trade-offs are real though. The verbosity is frustrating and inflates actual costs. Output speed lags behind the competition. English writing quality is serviceable but noticeably below the bar Claude and GPT set. And the 256K context window, while adequate, means Gemini has K2.5 beat by 4x for long-context work.
Our Score: 8.0 / 10
Kimi K2.5 doesn't replace Claude or GPT for most users. What it does is expand the options in a meaningful way — proving that open-weight models can compete at the frontier while costing a fraction of the price, and introducing Agent Swarm as a genuinely new paradigm for how AI handles complex tasks. That's worth paying attention to regardless of which model you use daily.