Kimi K2.5 vs GPT-5.3 Codex: Open-Weight Swarm vs the Codex Juggernaut

This comparison is about money as much as it is about benchmarks. GPT-5.3 Codex charges $28.00 per million output tokens. Kimi K2.5 charges $3.00. That is a 9.3x price difference on output - the token direction that dominates cost in most real workloads. And yet the benchmark picture is far from one-sided.

GPT-5.3 Codex is the model you call when autonomous coding accuracy is non-negotiable. Terminal Bench 2.0 at 77.3% versus K2.5's 50.8% is a 26.5-point gap - the single largest delta in this entire comparison. SWE-bench Verified at 80.0% versus 76.8% shows GPT-5.3 also leads on real-world bug fixing. But K2.5 counterattacks on math: AIME 2025 at 96.1 versus 88.5, a 7.6-point margin that puts Moonshot's model in a class of its own for mathematical competition problems.

The real question is whether GPT-5.3's coding advantages are worth paying nearly 10x more per output token, especially when K2.5 offers open weights, native vision, and an Agent Swarm system that GPT-5.3 cannot match.

Quick Comparison

Kimi K2.5: The Value Proposition That Fights Above Its Price

K2.5's trillion-parameter MoE architecture activates only 32 billion parameters per token across 61 layers with 384 experts. This design delivers a model that competes with the most expensive closed-source systems while costing a fraction of what they charge.

The math performance is the headline. AIME 2025 at 96.1 puts K2.5 ahead of GPT-5.3 by 7.6 points. HMMT at 95.4 confirms this is not overfitting to a single competition format - K2.5 genuinely has deeper mathematical reasoning capability. On LiveCodeBench v6 at 85.0, K2.5 also shows strong coding ability, though the gap between algorithmic problem-solving and production terminal work becomes clear when you look at Terminal Bench.

The Agent Swarm feature, trained via PARL reinforcement learning, is K2.5's unique structural advantage. On BrowseComp, the swarm configuration scores 78.4% - actually edging GPT-5.3's 77.9%. This is the only comparison where an open-weight model's multi-agent orchestration outperforms OpenAI's flagship on web research. The ability to deploy up to 100 sub-agents for complex decomposition tasks is something GPT-5.3 simply does not offer as a native capability.

MoonViT-3D adds native vision processing at 400 million parameters, handling images and video at native resolution. OCRBench at 92.3 and MMMU-Pro at 78.5 mean K2.5 can process documents, diagrams, and visual inputs without relying on external tooling. GPT-5.3 Codex has multimodal capabilities, but its primary optimization target is code generation, not broad visual understanding.

The pricing tells the story that the benchmarks cannot. At $0.60/$3.00 per million tokens on the Moonshot API, K2.5 is 5.8x cheaper on input and 9.3x cheaper on output. On OpenRouter at $0.45/$2.20, the savings widen further. For a team running 5 million output tokens per day, that is $15/day on K2.5 versus $140/day on GPT-5.3. Over a year: $5,475 versus $51,100.

GPT-5.3 Codex: When Terminal Accuracy Is Everything

GPT-5.3 Codex exists for one reason: it is the best model in the world at sitting in a terminal and writing, debugging, and deploying code autonomously. Terminal Bench 2.0 at 77.3% is not just a lead over K2.5's 50.8% - it is a different category of performance. This benchmark measures end-to-end terminal operations including file manipulation, build systems, testing frameworks, and deployment scripts. A 26.5-point gap means GPT-5.3 completes tasks that K2.5 cannot even attempt.

SWE-bench Verified at 80.0% reinforces the agentic coding story. This is real GitHub issues in real codebases - the kind of work that production engineering teams actually do. K2.5's 76.8% is respectable, but in a workflow where every failed resolution means a human engineer has to step in, that 3.2-point gap translates to meaningfully less manual intervention.

GPQA Diamond at 93.2 shows GPT-5.3 is not a one-trick coding model. That score leads K2.5's 87.6 by 5.6 points on graduate-level scientific reasoning. MMLU-Pro at 86.2 is within one point of K2.5's 87.1, making them effectively tied on broad knowledge.

The 400K context window gives GPT-5.3 a 1.56x advantage over K2.5's 256K. For agentic coding tasks where the model needs to hold an entire repository's context, this extra space matters. You can fit more files, more test outputs, and more conversation history into a single prompt. For an overview of how GPT-5.3 fits into OpenAI's broader lineup, see our guide on best AI coding CLI tools.

The cost is the trade-off. At $28.00 per million output tokens, GPT-5.3 Codex is the most expensive mainstream model for output generation. This pricing makes sense if you are using it for high-value coding tasks where each successful resolution saves hours of engineer time. It makes less sense for high-volume workloads, batch processing, or exploratory use cases where you are generating lots of tokens to find the right answer.

Benchmark Comparison

Terminal Bench 2.0 is the outlier that defines this comparison. Across every other benchmark, the gaps are single digits. Terminal Bench is a 26.5-point chasm. If your use case involves autonomous terminal work, there is no contest. If it does not, K2.5 wins on math and costs a fraction of the price.

Pricing Analysis

The output pricing gap is the one that matters most. In typical LLM workloads, output tokens dominate the bill because the model generates far more tokens than it reads in the prompt. At 9.3x cheaper output, K2.5 is in a fundamentally different cost tier. Teams self-hosting K2.5 with their own GPU infrastructure eliminate per-token costs entirely. For navigating inference costs across providers, see our free AI inference providers guide.

Kimi K2.5: Pros and Cons

Pros:

• AIME 2025 96.1 - outperforms GPT-5.3 by 7.6 points on competitive math
• 9.3x cheaper on output tokens ($3.00 vs $28.00 per million)
• Open weights under Modified MIT allow self-hosting and fine-tuning
• Agent Swarm (up to 100 sub-agents) edges GPT-5.3 on BrowseComp
• Native multimodal via MoonViT-3D for images and video
• MMLU-Pro 87.1 slightly leads GPT-5.3's 86.2

Cons:

• Terminal Bench 2.0 at 50.8% versus 77.3% is a massive deficit for autonomous coding
• SWE-bench 76.8% trails GPT-5.3's 80.0% on real codebase bug fixing
• GPQA Diamond 87.6 trails GPT-5.3's 93.2 on scientific reasoning
• 256K context versus GPT-5.3's 400K
• Smaller integration ecosystem compared to OpenAI's tooling

GPT-5.3 Codex: Pros and Cons

Pros:

• Terminal Bench 2.0 at 77.3% - unmatched autonomous terminal coding
• SWE-bench 80.0% for production-grade bug resolution
• GPQA Diamond 93.2 - elite scientific reasoning
• 400K context window for large codebase ingestion
• Deep integration with OpenAI's ecosystem and tooling

Cons:

• $28.00/M output tokens - the most expensive mainstream model
• Closed source with no self-hosting pathway
• AIME 2025 at 88.5 trails K2.5's 96.1 significantly on math
• No native Agent Swarm or multi-agent orchestration
• No published multimodal benchmarks at K2.5's level of detail

Verdict

Choose Kimi K2.5 if your workload is diverse - mixing math, reasoning, vision, and research tasks rather than pure autonomous coding. The 9.3x output price advantage means K2.5 is the economically rational choice for any use case where Terminal Bench does not dominate your requirements. The Agent Swarm gives you multi-agent orchestration that GPT-5.3 lacks, and the open weights mean you are never locked into a single provider's pricing decisions.

Choose GPT-5.3 Codex if autonomous terminal-based coding is your primary workflow and accuracy on the first attempt justifies the premium. The Terminal Bench gap is too large to dismiss, and for teams building AI-powered CI/CD pipelines, automated code review systems, or autonomous development agents, GPT-5.3 delivers measurably more completed tasks. Just make sure your budget accounts for the output token cost, because at volume, $28 per million tokens adds up fast. For an overview of how these models compare in agentic coding contexts, see our Codex vs Claude Code vs OpenCode comparison.

Sources

• Kimi K2.5 Technical Report - Moonshot AI
• GPT-5.3 Codex - OpenAI
• Terminal Bench 2.0 Leaderboard
• OpenRouter Model Pricing