Kimi K2.5 vs Claude Opus 4.6: Open-Weight Math Beast vs Proprietary Agent King

Two philosophies of building frontier AI, compressed into a single benchmark table. Kimi K2.5 is Moonshot AI's open-weight MoE colossus - one trillion total parameters, 32 billion active per token, Modified MIT license, native vision, and an Agent Swarm system that can spin up 100 sub-agents. Claude Opus 4.6 is Anthropic's closed-source flagship - undisclosed architecture, one million token context window in beta, and the strongest agentic coding results in the industry.

The split is dramatic. Kimi K2.5 demolishes Claude on mathematical reasoning: AIME 2025 at 96.1 versus 87.2, a gap of nearly nine points. Claude fires back on agentic benchmarks: SWE-bench Verified 80.8 versus 76.8, BrowseComp 84.0 versus 78.4. And then there is price. Kimi K2.5 costs $0.60 per million input tokens. Claude Opus 4.6 costs $5.00. That is an 8x difference on input alone.

This is not a case where one model dominates. It is a case where what you are building determines which model wins.

Quick Comparison

Kimi K2.5: The Open-Weight Polymath

Kimi K2.5 is a model that rewards close reading of the benchmark table. AIME 2025 at 96.1 is not just good - it is one of the highest scores published by any model, open or closed. HMMT at 95.4 confirms this is not a one-benchmark fluke. On math competition problems that trip up even frontier closed-source models, K2.5 is essentially solving nearly everything thrown at it.

The architecture is a 61-layer MoE with 384 total experts and 8 active per token, yielding 32 billion active parameters from a one-trillion-parameter pool. Moonshot trained it with PARL (a reinforcement learning method designed specifically for multi-agent coordination), which enables the Agent Swarm feature - the ability to orchestrate up to 100 sub-agents that decompose complex tasks. On BrowseComp, the Agent Swarm configuration hits 78.4%, while single-agent mode scores 60.6%. That 18-point gap shows the swarm is not a marketing feature; it is a genuine capability multiplier.

The vision system deserves attention too. MoonViT-3D is a 400-million-parameter vision encoder that handles native-resolution images and video frames without resizing. On OCRBench it scores 92.3 and on MMMU-Pro it hits 78.5. Claude Opus 4.6 has vision capabilities, but Anthropic has not published comparable multimodal benchmark numbers at the same level of detail.

At $0.60/$3.00 per million tokens on the Moonshot API - or even cheaper at $0.45/$2.20 on OpenRouter - this is frontier-class reasoning at mid-tier pricing. The Modified MIT license means you can download the weights, fine-tune them, and deploy commercially. For teams that want to own their inference stack, that changes the economics entirely.

Claude Opus 4.6: The Agentic Coding Standard

Claude Opus 4.6 does not win this comparison on math. It wins on the benchmarks that matter most for production software engineering and autonomous agent workflows.

SWE-bench Verified at 80.8% means Claude can resolve four out of five real GitHub issues when given the codebase and the issue description. That is the highest published score from any model at the time of writing. BrowseComp at 84.0% - a benchmark measuring autonomous web research and information synthesis - puts Claude ahead of K2.5's Agent Swarm result of 78.4%, and dramatically ahead of K2.5's single-agent 60.6%. GPQA Diamond at 91.3 also shows Claude's strength on graduate-level scientific reasoning, where it leads K2.5 by 3.7 points.

The million-token context window, currently in beta, is a structural advantage that no benchmark fully captures. If you are feeding entire codebases, long regulatory documents, or multi-session conversation histories into your prompts, Claude can hold roughly 4x more context than K2.5's 256K. For agentic coding tasks where the model needs to reason over hundreds of files simultaneously, this matters.

The cost is real. At $5.00/$25.00 per million tokens, Claude Opus 4.6 is the most expensive model in mainstream API access. A workload that costs $100 on Kimi K2.5 would cost $833 on Claude Opus for the same output volume. For high-volume applications, this is not a rounding error - it is a budget line item. But for teams building production agent systems where accuracy on the first pass saves engineering time, the price-per-correct-answer calculation can favor Claude. For context on how Claude stacks up against other proprietary options, see our ChatGPT vs Claude vs Gemini comparison.

Benchmark Comparison

The pattern is unmistakable. K2.5 owns math and competition-style reasoning. Claude owns agentic coding and web-based tasks. On MMLU-Pro and general knowledge, they are within two points of each other - essentially tied for practical purposes.

Pricing Analysis

At scale, this pricing gap is decisive. A team processing 10 million output tokens per day would pay $30/day with K2.5 or $250/day with Claude. Over a year, that is roughly $10,950 versus $91,250. K2.5's open weights also mean you can self-host and eliminate per-token costs entirely if you have the GPU infrastructure. For more on running large models locally, see our best local LLM tools guide.

Kimi K2.5: Pros and Cons

Pros:

• AIME 2025 96.1 and HMMT 95.4 - among the strongest math scores published by any model
• Open weights under Modified MIT license enable self-hosting and fine-tuning
• 8x cheaper than Claude Opus 4.6 on API pricing
• Native multimodal with MoonViT-3D for images and video
• Agent Swarm with up to 100 sub-agents via PARL training
• MMLU-Pro 87.1 slightly edges Claude's 85.8 on broad knowledge

Cons:

• SWE-bench 76.8% trails Claude's 80.8% on real-world coding
• BrowseComp 78.4% (Swarm) still behind Claude's 84.0%
• 256K context is a quarter of Claude's 1M window
• Younger ecosystem with less third-party tooling integration
• Agent Swarm requires specific orchestration setup

Claude Opus 4.6: Pros and Cons

Pros:

• SWE-bench 80.8% - the highest agentic coding score available
• BrowseComp 84.0% - best-in-class autonomous web research
• GPQA Diamond 91.3 shows elite scientific reasoning
• 1M context window (beta) for massive document and codebase processing
• Deep integration with Anthropic's tool-use and agent frameworks

Cons:

• $5.00/$25.00 per million tokens - the most expensive mainstream API
• Closed source with no self-hosting option
• AIME 2025 87.2 significantly trails K2.5's 96.1 on math
• No published multimodal benchmarks comparable to K2.5's OCRBench/MMMU-Pro
• Undisclosed architecture limits independent analysis

Verdict

Choose Kimi K2.5 if your work leans toward mathematical reasoning, scientific analysis, or any domain where raw problem-solving ability matters more than code-repair accuracy. The AIME and HMMT scores are not marginal leads - they represent a different tier of mathematical capability. The open weights, the 8x lower API cost, and the native multimodal stack make K2.5 the clear choice for budget-conscious teams and anyone who wants to own their model infrastructure. The Agent Swarm is also uniquely powerful for complex multi-step research tasks.

Choose Claude Opus 4.6 if you are building production agent systems that need to autonomously navigate codebases, fix real bugs, and research the web. The SWE-bench and BrowseComp leads are meaningful - they translate to fewer failed attempts and less human oversight in agentic workflows. The million-token context window is a genuine differentiator for workloads that require reasoning over very large inputs. The price premium is steep, but for high-stakes agentic tasks where correctness saves engineering hours, it can be justified. For the latest on how these and other frontier models rank, check our coding benchmarks leaderboard.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• Claude Opus 4.6 Model Card - Anthropic
• SWE-bench Verified Leaderboard
• OpenRouter Model Pricing