Kimi-K2 Thinking vs Claude 4.5 vs GPT-5 : The best LLM?
Kimi K2 Thinking Benchmarks explained
You know that phase where every new model claims to “surpass frontier systems”? Yeah, we’re in it again. This time it’s Kimi K2 Thinking, Moonshot AI’s latest reasoning-heavy model. They’re calling it a “thinking agent,” and to be fair, it’s not all talk.
https://medium.com/media/8f7cd509dde15b71e357bd392fa14ee5/href
I spent time digging into their benchmark table, the kind of dense, footnote-ridden data that most people scroll past. Let’s break it down, model by model, benchmark by benchmark, without the PR sugarcoat.
1. Reasoning Tasks: Math, Logic, and Tool Use
Benchmarks like Humanity’s Last Exam (HLE), AIME 2025, HMMT 2025, IMO-AnswerBench, and GPQA-Diamond test how well a model can actually think, structured reasoning, math proofs, symbolic manipulation, that sort of thing.
Without tools, GPT-5 still has a slight edge. For instance, on HMMT 2025, GPT-5 scores 93.3 while K2 hits 89.4. Not bad, but not dethroning anyone yet. The moment you allow tools, Python, search, scratchpads, K2 starts punching above its weight. On HLE (with tools) it scores 44.9, beating GPT-5’s 41.7. On AIME 2025, both hit near-perfect (99+), and on IMO-AnswerBench, K2 actually edges past GPT-5 (78.6 vs 76).
It means K2’s reasoning engine thrives when it can act, use external logic & tools. But pure, pen-and-paper logic? GPT-5’s still cleaner.
2. General Knowledge: The Textbook Stuff
Now we move to MMLU-Pro, MMLU-Redux, Longform Writing, and HealthBench. This is the domain of memorization and generalization, not deep reasoning, but knowing things and writing about them coherently.
Here, GPT-5 and Claude still rule the hill. GPT-5 gets 87.1 on MMLU-Pro; K2 comes close at 84.6. Not a big gap, but consistent. Claude edges ahead in writing tasks, 79.8 vs K2’s 73.8, though interestingly, K2 beats GPT-5 (71.4).
Then there’s HealthBench, which exposes domain weaknesses. GPT-5 scores 67.2, K2’s at 58.0, and Claude collapses to 44.2. It’s not that K2 doesn’t “know” medicine, it’s just not as tuned for structured factual reasoning in that area. Think of it as a generalist pretending to be a doctor. GPT-5 actually studied for the exam.
So K2’s general-knowledge brain is good, not great. It can talk, but you can tell it’s more comfortable solving than explaining.
3. Agentic Search: Where K2 Really Shows Off
These are the BrowseComp, Seal-0, FinSearchComp-T3, and Frames benchmarks. Think of them as scavenger hunts for LLMs, models must browse the web, collect data, plan multi-step searches, sometimes even simulate reasoning chains across hundreds of steps.
This is where K2 comes alive. On BrowseComp, it hits 60.2, comfortably ahead of GPT-5’s 54.9 and way above Claude’s 24.1 (yeah, ouch). On Frames, K2 gets 87.0, again beating GPT-5 (86.0). The only case where GPT-5 squeaks ahead is FinSearchComp-T3 (48.5 vs 47.4), and even there it’s marginal.
You can tell K2’s architecture was designed for this kind of work, agentic reasoning, tool coordination, contextual persistence.
If I had to summarize:
- GPT-5 feels like a brilliant solo thinker who occasionally Googles something.
- K2 feels like a decent thinker with an excellent browser and discipline.
- Claude… well, Claude’s still learning how to multitask.
4. Coding: Somewhere Between Genius and Student
Coding tasks are brutal to benchmark. You’ve got SWE-bench, LiveCodeBench, OJ-Bench, SciCode, Terminal-Bench, and more. Each tests something different, bug fixing, code completion, or simulated execution.
The short version: K2’s not the best, but it’s damn close. On SWE-bench Verified, it trails GPT-5 (71.3 vs 74.9) and Claude (77.2). But move to multilingual coding and suddenly it’s stronger than GPT-5–61.1 vs 55.3 , though still behind Claude (68.0).
In SciCode (scientific programming) and Terminal-Bench, K2 again edges GPT-5 slightly. The real kicker is LiveCodeBench v6, where K2 gets 83.1 compared to GPT-5’s 87.0, but miles ahead of Claude’s 64.0.
K2 can code. It’s not perfect, but it’s stable, multilingual, and adaptive. It behaves like an engineer who knows what to look up instead of memorizing syntax.
5. What the Table Doesn’t Tell You
Benchmarks can be misleading. Half the table has asterisks for “re-tested” results. The evaluation setups vary, context limits, token budgets, even tool availability differ slightly. GPT-5 often gets “Pro” settings; K2 uses internal reasoning limits like 96k tokens.
Also, K2 blocks web access to some datasets (like Hugging Face) for fairness, meaning actual real-world performance might be slightly higher than reported. So, numbers are numbers. You don’t get the full picture until you run these things live.
Final Thoughts
Kimi K2 Thinking is not “the new frontier,” but it’s definitely the first open model that feels like it belongs in the same ring as GPT-5 and Claude 4.5. It’s especially impressive on reasoning with tools and agentic search, areas most models still struggle to get right.
But it’s not flawless. Without tools, it sometimes loses composure. On general tasks, GPT-5’s training depth shows. On coding, Claude still wins by a small but steady margin.
For me, that’s a bigger deal than the numbers suggest. It’s one of the first times an open model can play on the same board as frontier ones and not get humiliated. Maybe that’s what “thinking” should mean.
Kimi-K2 Thinking vs Claude 4.5 vs GPT-5 : The best LLM? was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
