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Kimi K2 Thinking vs GPT-5 vs Claude Sonnet 4.5 – Which is better?

kimi k2 thinking
Benchmarks show Kimi K2 Thinking out-performing GPT-5 and Claude Sonnet 4.5 in some scenarios. Is it the better model?

Kimi K2 Thinking is a recent update to Moonshot AI’s Kimi K2 open-source model, designed specifically for deep reasoning, agentic workflows, and advanced coding tasks. According to the official announcement, Kimi K2 Thinking can perform between 200 and 300 sequential tool operations without needing any human intervention. Besides, it also boasts a huge parameter count, native quantization, and unprecedented stability for long, autonomous tool chains.

But perhaps the most inquisitive aspect of this release is how Kimi K2 thinking actually outperforms GPT-5 (try here) and Claude Sonnet 4.5 (try here) in key benchmarks, which makes a comparison between them all the more curious. So without wasting a moment, let’s get straight into it.

Introduction to Kimi K2 Thinking

Kimi interface and “Thinking” toggle

The model Kimi K2 Thinking is developed by Moonshot AI, a Chinese AI startup, and was released in early November 2025 (November 6).

Here are the main features and claims:

Kimi K2 Thinking Architecture & design

Kimi K2 Thinking is a Mixture-of-Experts (MoE) model with about one trillion total parameters, with roughly 32 billion active per inference. It is built for long-horizon reasoning and multi-tool workflows, reportedly handling 200–300 sequential tool calls without human intervention. The model supports very large context windows, up to 256,000 tokens. It is released on HuggingFace under a Modified MIT license, effectively open-source but requiring attribution for large-scale commercial use.

Kimi K2 Thinking Functional focus

The “Thinking” label highlights the model’s focus on reasoning and agentic workflows—tool use, planning, and code editing—rather than simple next-token prediction. It is positioned as a coding and reasoning model, claiming state-of-the-art results in reasoning, tool use, coding benchmarks, and web-search/agentic tasks. It targets developer and enterprise use cases such as code generation, multi-step workflows, and tool chaining, with efficiency as a core design goal.

Kimi K2 Thinking Benchmarks & performance claims

Kimi K2 Thinking vs GPT-5 (High) vs Claude Sonnet 4.5 benchmarks
  • K2 Thinking reportedly achieved 71.3% on SWE-Bench Verified (a real-world coding benchmark) and 83.1% on LiveCodeBench v6.
  • On web-agentic reasoning benchmarks (BrowseComp) it achieved 60.2%, ahead of GPT-5’s 54.9%.
  • On “Humanity’s Last Exam (HLE)” it scored 44.9%.
  • Efficiency-wise, the usage cost claims are significantly lower than proprietary models: e.g., $0.15 / 1 M input tokens (cache hit), $2.50 / 1 M output tokens.

Kimi K2 Thinking Licensing & openness

  • The weights, code, API are publicly available on HuggingFace and Moonshot’s platform.
  • The Modified MIT licence grants commercial usage but has a requirement: if the product serves >100 million monthly active users or >US$20 million/month revenue, the UI must display “Kimi K2”.
  • This openness is significant because many frontier models are closed-weight or proprietary.

Kimi K2 Thinking vs GPT-5

Now let’s compare Kimi K2 Thinking with GPT-5 along key dimensions: architecture, coding benchmarks, tool use and workflows, context window, licensing/cost, and practical applicability.

Architecture & design

  • GPT-5 (from OpenAI) is described as a state-of-the-art general-purpose frontier LLM. According to OpenAI, GPT-5 “is much smarter across the board … particularly in math, coding, visual perception, and health.”
  • On benchmarks, GPT-5 achieved 74.9% on SWE-Bench Verified (real-world Python coding tasks) under “thinking” mode.
  • In the Aider Polyglot benchmark it reached 88% (reportedly).
  • Architecture details (parameter size, MoE vs dense) are not fully public.
  • In contrast, Kimi K2 Thinking explicitly uses MoE architecture (1 trillion parameters, 32 billion active) and emphasises multi-tool workflows.

Coding & benchmark performance

  • GPT-5: SWE-Bench Verified — 74.9%.
  • Kimi K2 Thinking: SWE-Bench Verified — 71.3%.
    • At first glance, GPT-5 holds a lead (74.9% vs 71.3%).
  • However, Kimi K2 Thinking’s other metrics (agentic tool use, extremely large context/batch tool chaining) suggest different strength areas (multi-step agentic workflows).
  • One report claimed that K2 Thinking “crushes GPT-5” in key reasoning/coding benchmarks, e.g., BrowseComp (60.2% vs GPT-5’s 54.9%).
  • It’s worth stressing that different benchmarks, contexts and disclosure transparency vary. Some GPT-5 numbers come from OpenAI; some Kimi numbers from third-party or company-released data.
  • In terms of stability: GPT-5 appears to benefit significantly from its “thinking” or reasoning-enabled mode (chain of thought). For example, in blog commentary: “Reasoning gives a huge boost to GPT-5: +22.1 points on SWE-bench and +61.3 points on Aider Polyglot.”
  • So the configuration (thinking mode vs standard) matters heavily.

Tool use, agentic workflows & context window

  • GPT-5 claims high performance in “real-world coding (74.9% on SWE-bench Verified, 88% on Aider Polyglot)”.
  • Kimi K2 Thinking emphasises “200-300 sequential tool calls without human interference … reasoning across hundreds of steps”.
  • Context window: Kimi’s claim up to 256,000 tokens. For GPT-5, specific public context-window size details are less clearly documented in open sources.
  • The meaning being: Kimi may have an edge in tasks requiring long context / multi-file dependencies / long chains of reasoning and tool orchestration.

Licensing, openness & cost-efficiency

  • GPT-5: proprietary model, usage via OpenAI’s commercial API. Cost details are proprietary.
  • Kimi K2 Thinking: open-weight, Modified MIT license; cheaper cost claims: e.g., $0.15 / 1 M input tokens (cache hit).
  • From an enterprise perspective, Kimi may offer competitive cost/performance trade-offs especially for large-scale deployments.

Practical implications for coding / engineering teams

  • If you require: high accuracy in standard code generation (especially shorter tasks) and you are willing to use proprietary API, GPT-5 appears a strong choice.
  • If you require: long context, agentic workflows, multi-tool orchestration, open-source weights, and cost-sensitive deployments, then Kimi K2 Thinking becomes very attractive.
  • One area to watch: maturity of tooling, ecosystem, bug/hallucination-rates, support/community around Kimi vs GPT-5.

In short: GPT-5 likely retains advantage in raw coding-benchmark performance (for shorter tasks) and presumably in production maturity, whereas Kimi K2 Thinking brings strong competition — particularly in multi-step reasoning and tool-use workflows — plus openness and cost advantages. As the “open-weight frontier” arms race advances, Kimi may increasingly narrow or even surpass proprietary models in more domains.

Kimi K2 Thinking vs Claude Sonnet 4.5

Now we compare Kimi K2 Thinking with another competitor: Claude Sonnet 4.5 (from Anthropic).

Architecture & design

  • Claude Sonnet 4.5 is characterized as a “new generation” coding/agentic model from Anthropic. According to Anthropic: “Sonnet 4.5’s edit capabilities are exceptional … we went from 9% error rate on Sonnet 4 to 0% on our internal code-editing benchmark.”
  • It emphasises tool use, parallel tool execution, long-context reasoning. Example: “running multiple bash commands at once”.
  • Benchmarking data: On SWE-Bench Verified, it achieved 77.2% under standard conditions and 82.0% with “parallel test-time compute”. 
  • On OSWorld (computer-use skills) it achieved 61.4% — “significantly ahead of next-best model”.
  • So Claude Sonnet 4.5 appears to set a new high-bar for coding/engineering tasks (among closed-weight models).

Coding & benchmark performance

  • Claude Sonnet 4.5: SWE-Bench Verified ~77.2% standard, 82.0% enhanced.
  • Kimi K2 Thinking: SWE-Bench Verified ~71.3% (according to Moonshot) — thus in direct SWE-bench measure, Claude appears ahead.
  • However, in certain categories such as agentic web search (BrowseComp) Kimi had 60.2% vs Claude’s 24.1% in one report (though we should treat that with caution).
  • In terms of stability and predictability: One blog noted: “Claude Sonnet 4.5 delivers steady, high accuracy across tasks without requiring special modes or tuning … GPT-5, on the other hand, shows a bigger jump when its ‘thinking’ mode is enabled… for teams prioritizing predictability, that stability matters.”
  • So from a coding/engineering perspective, Claude Sonnet 4.5 appears strong across the board, particularly for code-editing, multi-file workflows, bug detection, agentic use.

Tool use, agentic workflows & context window

  • Claude Sonnet 4.5 emphasises efficient tool-execution (“parallel tool execution”, long contexts) and reduced error rate in code editing.
  • Kimi K2 Thinking emphasises long context, up 256k tokens, 200-300 tool calls, open-weight. Kimi may have the edge for extremely large context / custom tool workflows.
  • On the question of context window, explicit comparative numbers for Claude’s context window are less publicly detailed, though Sonnet 4.5 is described as improving “long-context tasks” in the Anthropic blog.
  • For agentic coding workflows (e.g., bug detection, multi-file refactoring), Claude appears to lead (higher benchmark). Kimi brings an interesting combination of openness and large-scale capabilities.

Licensing, openness & cost-efficiency

  • Claude Sonnet 4.5: proprietary model, usage via Anthropic’s API/enterprise offerings.
  • Kimi K2 Thinking: open-weight, Modified MIT license, cheaper cost claims.
  • In scenarios where cost and flexibility matter (on-premises deployment, custom tool integration, open model communities), Kimi may be advantageous.

Practical implications for coding/engineering teams

  • If you are an engineering team requiring top commercial performance for code generation, editing large codebases, bug detection, and multi-file refactoring, then Claude Sonnet 4.5 appears to be near the top.
  • If you are a team that values open-source weights, the ability to integrate/customise heavily (tool-orchestration, large context window, on-premises), then Kimi K2 Thinking becomes very attractive.
  • The trade-off may be: proprietary vs open, cost vs maturity, ecosystem vs flexibility.

Model Feature Comparison Table

Here is a consolidated comparison of the three models across key dimensions:

Benchmarks are approximate and based on publicly reported data; direct head-to-head comparisons may vary depending on the mode, dataset, and tool integration.

The Bottom Line

Kimi K2 Thinking represents the leading edge of open-source, agentic AI models for advanced reasoning and coding automation, with strong scores and unique features such as INT4 quantization and extreme long-horizon stability. GPT-5 sets the bar for coding benchmarks, context capacity, and efficiency, while Claude Sonnet 4.5 provides a well-rounded, safe, and deeply integrated development experience suitable for autonomous coding agents. While Kimi K2 Thinking slightly trails GPT-5 and Claude Sonnet 4.5 in coding-only benchmarks, its open architecture, agentic depth, and flexible quantization make it a standout choice for research and project automation by technical users.

That said, if you’re looking for a place where you don’t have to stick to one model or one ‘way,’ consider Bind AI, which offers you access to Claude Sonnet 4.5,  GPT-5, Gemini 2.5 Pro, OpenAI o3, and more + a full, cloud IDE for your coding workflows. Try Bind AI now!