Kimi K2.5 vs Gemini 3.1 Pro: Math Prodigy vs the Knowledge Generalist

This is one of the tightest comparisons in the current frontier landscape. Kimi K2.5 and Gemini 3.1 Pro trade wins across nearly every benchmark category, with neither model establishing clear dominance. Gemini takes the knowledge and reasoning benchmarks - MMLU-Pro 90.1 versus 87.1, GPQA Diamond 94.3 versus 87.6. K2.5 fires back on competition math - AIME 2025 96.1 versus 91.0, HMMT 95.4 versus 93.8. And on agentic tasks, K2.5's Agent Swarm actually flips the script: BrowseComp 78.4% versus Gemini's 59.2%.

Both are multimodal. Both handle enormous contexts. Both are priced for real-world use. The difference comes down to what you optimize for and whether you need open weights.

Quick Comparison

Kimi K2.5: Math Dominance and Agent Swarm Advantage

K2.5's mathematical reasoning is the strongest aspect of this model against any competitor. AIME 2025 at 96.1 leads Gemini by 5.1 points. HMMT at 95.4 leads by 1.6 points. These are competition math benchmarks where even small improvements require meaningful advances in chain-of-thought reasoning and problem decomposition. K2.5 does not just edge past Gemini on math - it holds a comfortable margin.

The architecture delivers this through a 1T-parameter MoE with 384 experts, 8 active per token, and 61 layers. The PARL-trained Agent Swarm capability is where K2.5 creates the most surprising gap in this comparison. On BrowseComp - which tests the ability to find obscure information across the web through multi-step research - K2.5's swarm of up to 100 sub-agents scores 78.4% versus Gemini's 59.2%. That is a 19.2-point advantage, the largest delta in this entire matchup. In single-agent mode, K2.5 scores 60.6% on BrowseComp, nearly identical to Gemini. The swarm is doing real work.

SWE-bench Verified at 76.8% versus Gemini's 76.2% is essentially a tie - both models resolve roughly three out of four real GitHub issues. LiveCodeBench v6 at 85.0 shows strong competitive coding ability, though Gemini's Codeforces rating of 2439 (K2.5 has not published a comparable number) suggests Google's model has deeper competitive programming capability.

The vision system is another K2.5 strength worth highlighting. MoonViT-3D processes images and video at native resolution, scoring 92.3 on OCRBench and 78.5 on MMMU-Pro. Gemini 3.1 Pro is also natively multimodal and handles images, video, and audio, so this is not a category where either model lacks capability - but K2.5's published benchmarks on visual tasks are specific and strong.

Pricing favors K2.5 substantially: $0.60/$3.00 versus Gemini's $2.00/$12.00. That is 3.3x cheaper on input and 4x cheaper on output. With OpenRouter pricing at $0.45/$2.20, the gap widens to 4.4x on input and 5.5x on output.

Gemini 3.1 Pro: The Knowledge and Reasoning Ceiling

Gemini 3.1 Pro holds the two benchmark numbers that matter most for broad intellectual capability. MMLU-Pro at 90.1 is the highest score in this comparison by a 3-point margin - a benchmark covering 57 subjects from abstract algebra to virology at professional difficulty. GPQA Diamond at 94.3 leads K2.5 by 6.7 points, testing graduate-level science questions that are designed to mislead even domain experts.

These are not narrow benchmarks. They measure the kind of broad, deep knowledge that determines whether a model can function as a reliable research assistant across domains. A model scoring 94.3 on GPQA Diamond is getting correct answers on questions that PhD students in the relevant field answer wrong. For Gemini to hold a 6.7-point lead here indicates a genuinely deeper knowledge representation, likely a product of Google's training data advantages.

The 1M token context window is Gemini's structural differentiator. At 4x K2.5's 256K, it can ingest entire codebases, full-length books, hours of conversation history, or massive document collections in a single prompt. For retrieval-augmented generation workloads where stuffing more context improves answer quality, this is not a marginal advantage. Google's multi-modal context handling also extends to audio and video natively.

Codeforces at 2439 is a strong competitive programming result. Without a published Codeforces rating from K2.5, direct comparison is impossible, but 2439 places Gemini in the top tier of coding models. Combined with SWE-bench at 76.2% (within 0.6 points of K2.5), Gemini is a strong all-around coding model even if it is not specifically optimized for it. For how Gemini fits into the broader chatbot landscape, see our ChatGPT vs Claude vs Gemini comparison.

At $2.00/$12.00, Gemini is more expensive than K2.5 but meaningfully cheaper than Claude Opus 4.6 or GPT-5.3 Codex. It occupies a middle tier - not the cheapest option, but not the premium tier either.

Benchmark Comparison

Two models, two different shapes of excellence. Gemini dominates knowledge-intensive evaluation. K2.5 dominates competition math and multi-agent web research. SWE-bench is a dead heat. The benchmark you care about most determines your pick.

Pricing Analysis

The cost gap is significant but not as dramatic as the K2.5-vs-Claude comparison. Teams choosing between these two are more likely deciding based on benchmark profiles and context window needs than on price alone. That said, the 4x output cost difference compounds quickly at high volume. K2.5's open weights again provide the escape valve - self-host on your own infrastructure and the marginal cost per token drops to zero. For teams evaluating GPU hardware for local inference, see our LLMFit tool comparison.

Kimi K2.5: Pros and Cons

Pros:

• AIME 2025 96.1 and HMMT 95.4 - clear math superiority over Gemini
• Agent Swarm BrowseComp 78.4% crushes Gemini's 59.2% on web research
• 3.3x cheaper input and 4x cheaper output than Gemini
• Open weights under Modified MIT for self-hosting and fine-tuning
• SWE-bench 76.8% slightly edges Gemini's 76.2%
• Native video understanding via MoonViT-3D

Cons:

• GPQA Diamond 87.6 trails Gemini's 94.3 by a wide margin
• MMLU-Pro 87.1 trails Gemini's 90.1 on broad knowledge
• 256K context is one-quarter of Gemini's 1M window
• No published Codeforces rating to compare competitive programming
• Smaller ecosystem and fewer third-party integrations than Google's platform

Gemini 3.1 Pro: Pros and Cons

Pros:

• MMLU-Pro 90.1 and GPQA Diamond 94.3 - the highest knowledge benchmark scores in this comparison
• 1M token context window for massive input processing
• Codeforces 2439 shows elite competitive programming
• Google ecosystem integration (Vertex AI, Google Cloud, Workspace)
• Native multimodal including audio processing

Cons:

• BrowseComp 59.2% is 19.2 points behind K2.5's Agent Swarm
• AIME 2025 91.0 trails K2.5's 96.1 on competition math
• $2.00/$12.00 pricing is 3-4x more expensive than K2.5
• Closed source with no self-hosting option
• SWE-bench 76.2% is slightly behind K2.5

Verdict

Choose Kimi K2.5 if mathematical reasoning and multi-agent research are your primary workloads. The AIME and HMMT leads are substantial, and the Agent Swarm's 19.2-point advantage on BrowseComp is the kind of gap that changes what tasks are feasible. The pricing at 3-4x cheaper and the open-weight license make K2.5 the rational choice for teams that value autonomy and cost efficiency, especially if GPQA-level knowledge breadth is not the bottleneck.

Choose Gemini 3.1 Pro if you need the broadest and deepest knowledge representation available, the largest context window on the market, or tight integration with Google's cloud platform. MMLU-Pro at 90.1 and GPQA Diamond at 94.3 are not close calls - Gemini knows more across more domains than K2.5 does. If your application involves synthesizing information from very long documents or requires answering expert-level questions across diverse fields, Gemini is the stronger foundation. For how these models rank in the broader frontier landscape, check our open-source LLM leaderboard.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• Gemini 3.1 Pro Model Card - Google DeepMind
• BrowseComp Benchmark - OpenAI
• OpenRouter Model Pricing