Kimi K3.1 Pro vs Claude Opus 5.5 Comparison

Kimi K3.1 Pro vs Claude Opus 5.5: 2.8T Open Weights vs Proprietary Frontier

Kimi K3.1 Pro delivers comparable terminal coding and leads long-horizon software execution at a lower token cost, while Claude Opus 5.5 maintains a substantial lead on pure scientific reasoning and closed-book logic. The choice between them comes down to whether your workflow prioritizes verifiable open-weight deployment or closed-model reasoning depth.

Key Takeaways

• Coding parity: Kimi K3.1 Pro scores 65.7% on Terminal-Bench 4.0, trailing Claude Opus 5.5 (66.4%) by just 0.7 percentage points, while outperforming it on SWE Marathon (51.7% vs 46.3%).
• Reasoning divergence: Claude Opus 5.5 significantly leads on non-tool and scientific evaluations, posting 59%–63.3% on Terminal-Bench-Science 0.1 compared to Kimi's 34%–38.1%.
• Architectural divide: Kimi K3.1 Pro is an open-weight 2.8-trillion-parameter Mixture-of-Experts (MoE) model activating 104 billion parameters per token; Claude Opus 5.5 is a closed, proprietary system.
• Serving overhead: Kimi K3.1 Pro standard inference runs at 33 tokens per second with higher output token verbosity, whereas Claude Opus 5.5 generates at roughly 104 tokens per second.
• Token economics: Moonshot lists Kimi K3.1 Pro at $2.00 input and $10.00 output per 1M tokens, roughly half the baseline token rates of Claude Opus 5.5 ($4.00/$20.00).

Kimi K3.1 Pro vs Claude Opus 5.5 at a Glance

Evaluation Dimension	Kimi K3.1 Pro	Claude Opus 5.5	Verification Status
Developer	Moonshot AI	Anthropic	Confirmed
Release Date	October 23, 2026	September 22, 2026	Confirmed
Model Architecture	Sparse MoE (2.8T total / 104B active)	Proprietary Transformer	Architecture unverified for Opus 5.5
Weights Availability	Open weights on Hugging Face (Kimi License)	Closed / Hosted API only	Confirmed
Context Window	1,048,576 tokens (~1M)	1,000,000 tokens (~1M)	Confirmed
Maximum Output Tokens	Up to 1,048,576 tokens (default 131,072)	128,000 tokens (300K Batch beta)	Confirmed
Terminal-Bench 4.0	65.7% (Moonshot report) / 67% (AA run)	66.4% (Moonshot table) / 60% (AA run)	Third-party measured
SWE Marathon (v1.1)	51.7%	46.3%	Vendor-reported
Terminal-Bench-Science 0.1	34% (AA run) / 38.1% (Vendor test)	59% (AA run) / 63.3% (Vendor test)	Third-party measured
HLE-Diamond	37% (AA v5) / 39.4% (Closed-book)	68% (AA v5) / 60.9% (Closed-book)	Third-party measured
Artificial Analysis Index	46 (Index v5.0.1)	59 (Index v5.0.1)	Independent benchmark
Standard Token Rates	$2.00 input / $10.00 output (per 1M)	$4.00 input / $20.00 output (per 1M)	Confirmed
Prompt Cache Hit Price	$0.20 per 1M tokens	$0.20 per 1M tokens	Confirmed
Average Speed	33 tokens/second	104 tokens/second	Measured by Artificial Analysis

Coding and Tool Execution

The most visible battleground between Kimi K3.1 Pro and Claude Opus 5.5 centers on developer tool-use and coding environments. Moonshot heavily post-trained K3.1 Pro across hundreds of downstream engineering environments.

On Terminal-Bench 4.0, which grades command-line file modification, process execution, and testing within containerized environments, the models run neck-and-neck. Moonshot reported Kimi K3.1 Pro at 65.7%, trailing Claude Opus 5.5 at 66.4%. In later evaluations conducted by Artificial Analysis using alternate harness parameters, Kimi K3.1 Pro reached 67% compared to Opus 5.5's 60%.

Source: @alexgetmancom

In multi-turn, persistent repository modifications, Kimi takes the lead. On SWE Marathon v1.1, Kimi K3.1 Pro scored 51.7%, topping Claude Opus 5.5's 46.3%. Engineers testing both models in standard Claude Code harnesses have noted distinct failure patterns. In community tests involving planted code-review bugs, Kimi displayed higher precision (0.67 vs 0.50), avoiding the decoy issues that Opus flagged as genuine defects.

However, Claude Opus 5.5 demonstrates higher reliability on under-specified, open-ended tasks. When software requirements lack explicit test assertions, Opus 5.5 handles architectural ambiguities with fewer dead-end iterations.

Scientific Knowledge and Pure Reasoning

Where Kimi holds ground in pragmatic code manipulation, it stumbles significantly on abstract logic and pure scientific problem solving.

On Terminal-Bench-Science 0.1, which tasks models with operating laboratory software and executing computational experiments across physics, chemistry, and mathematics, Claude Opus 5.5 reached 59% (63.3% in vendor comparisons), while Kimi K3.1 Pro managed only 34% (38.1% in vendor tests).

Source: @kimmonismus

The separation widens when tools and interpreters are removed. On the Humanity's Last Exam (HLE-Diamond) benchmark, Claude Opus 5.5 registered 68% on the Artificial Analysis v5 Index, whereas Kimi K3.1 Pro dropped to 37%. Independent analysis indicates that while Kimi's reinforcement learning pipeline successfully taught the model to solve multi-step problems by iteratively testing terminal outputs, it did not instill equivalent depth in closed-book reasoning.

For developers evaluating cross-domain workflows, our earlier review of the Kimi K3 runtime integration and 3D synthesis observed similar patterns: strong tool utilization paired with a dependency on environmental feedback to correct errors.

Model Architecture and Context Handling

The technical makeup of the two models highlights different engineering philosophies. Kimi K3.1 Pro uses a sparse Mixture-of-Experts architecture containing 2.8 trillion total parameters, routing to 896 experts per MoE layer and activating 16 experts (104 billion parameters) per token. Moonshot also replaced full-attention Gated MLA layers from K3 with Gated DeepSeek Sparse Attention (Gated DSA) layers to manage attention overhead across its 1-million-token context window.

Claude Opus 5.5 operates as a closed, proprietary service. While Anthropic has not released its exact parameter counts, it provides high-throughput generation at roughly 104 tokens per second, compared to Kimi K3.1 Pro's 33 tokens per second.

Both systems support a 1M context window, but their generation limits differ:

• Kimi K3.1 Pro: Supports text and image inputs with maximum generation capacity up to 1,048,576 tokens (131,072 tokens by default).
• Claude Opus 5.5: Supports text and image inputs with a 128,000-token maximum generation limit (extended to 300,000 tokens in the Batch API beta).

For organizations deploying private models, Kimi's architecture introduces severe hardware requirements. Weighing 1.56 TB in 4-bit precision, serving the unpruned model locally requires an 8-way NVIDIA B300 system with 2.3 TB of GPU memory.

API Latency, Verbosity, and Cost Realities

Standard per-token pricing shows a sharp divide. Kimi K3.1 Pro lists at $2.00 per 1M input tokens and $10.00 per 1M output tokens, cutting Claude Opus 5.5's rates ($4.00 input / $20.00 output) in half. Prompt caching rates match at $0.20 per 1M tokens.

Yet end-to-end task costs do not always reflect that 50% discount. Benchmark traces show Kimi K3.1 Pro generates substantially more tokens to solve complex prompts. In Artificial Analysis Intelligence Index runs:

• Kimi K3.1 Pro: Generated an average of 72.2k output tokens (44.2k reasoning tokens) per task, taking 36.3 minutes per problem at an effective task cost of $3.68.
• Claude Opus 5.5: Averaged 173k output tokens (120k reasoning tokens) per task, completing runs in 27.7 minutes at an effective cost of $10.04.

While Kimi completed these benchmark evaluations at roughly one-third the aggregate cost, its slower 33 tok/s execution speed increases total wall-clock runtime for deep reasoning sessions. Additionally, developer complaints surfaced following Kimi's rollout regarding strict rate limits on consumer subscription plans, which prompted Moonshot to issue an apology and quota resets on October 25, 2026.

Developers looking to integrate inference without managing private clusters can access Kimi K3.1 Pro directly via API on kie.ai.

Specialized Capabilities: 3D, Cyber, and Collaboration

The models show distinct separation on vertical tasks:

1. 3D Scene and CAD Generation: Kimi K3.1 Pro was post-trained directly on parametric modeling. On BenchCAD, Kimi scored 92.7% Voxel IoU with tools, approaching Opus 5.5 (96.2%). While Kimi demonstrates proficiency in writing direct Three.js, Blender, and CAD scripts, independent community tests on the AA-3D arena suggest its interactive mesh manipulation ranks lower than its raw coding marks.
2. Cybersecurity Safeguards: Moonshot documented that scaling reinforcement learning inadvertently elevated the base model's offensive capabilities to a 90.5% pass@3 on exploit benchmarks. Moonshot stripped cyber exploitation data from mid-training and RL sets prior to release, lowering pass@3 rates to 81.0% to minimize offensive misuse risks.
3. Multi-Agent Collaboration: In academic testing on the Collaborative Intelligence Benchmark (SLE), frontier closed models exhibited stronger cooperative skills. Default configurations scored Claude Opus 5.5 at 71.0 points and Kimi K3.1 Pro at 59.9 points, with Kimi falling behind primarily in proactive assistance and resolving conflicting constraints.

For more background on how frontier open systems stack up against competing Western closed offerings, see our comparison of Kimi K3 against Claude models.

Which One Should You Use?

Choose Kimi K3.1 Pro if:

• You require full control over deployment: Your compliance posture or security mandate prohibits sending intellectual property to external closed cloud providers, and your organization has the hardware necessary to run 1.56 TB checkpoints.
• You are running automated coding agents with terminal harnesses: Your workloads rely heavily on multi-turn code generation, automated regression runs, and environment feedback loops where Kimi matches or outpaces Opus on SWE Marathon.
• You need native parametric 3D generation: Your pipeline involves generating procedural Blender, CAD, or Three.js assets directly from natural language prompts.

Choose Claude Opus 5.5 if:

• Your work involves complex scientific or mathematical reasoning: You require high closed-book reasoning accuracy and advanced scientific computing where Kimi trails by more than 20 points.
• Interactive latency is critical: You are serving real-time user-facing applications that cannot absorb Kimi's 33 tok/s generation throughput and long time-to-first-token intervals.
• You require out-of-the-box enterprise cloud hosting: You depend on unified billing and compliance within standard US cloud providers such as AWS Bedrock or Google Cloud Vertex AI.

Frequently Asked Questions

Is Kimi K3.1 Pro better than Claude Opus 5.5 for coding?

Kimi K3.1 Pro outperforms Claude Opus 5.5 on long-horizon engineering benchmarks like SWE Marathon (51.7% vs 46.3%), while Claude Opus 5.5 retains a slight edge on Terminal-Bench 4.0 (66.4% vs 65.7%). In code review tasks, independent tests show Kimi exhibits higher bug-detection precision with fewer false alarms, though Opus 5.5 handles abstract architectural trade-offs with greater consistency.

Are Kimi K3.1 Pro weights available for self-hosting?

Kimi K3.1 Pro weights are publicly available on Hugging Face under the Kimi K3.1-Pro License, whereas Claude Opus 5.5 remains a closed proprietary model accessible only via API and managed platforms. Commercial use of Kimi is permitted, though companies with more than $20M in annual revenue or products serving over 100M monthly active users must sign separate agreements.

What is the context window for Kimi K3.1 Pro and Claude Opus 5.5?

Both Kimi K3.1 Pro and Claude Opus 5.5 support a 1-million-token context window, though Kimi supports up to 1,048,576 output tokens while Opus 5.5 caps standard generation at 128,000 tokens. Both models support multimodal text and image inputs.

Which model performs better on pure scientific reasoning?

Claude Opus 5.5 holds a significant advantage in scientific tasks, scoring 63.3% on Terminal-Bench-Science 0.1 compared to 38.1% for Kimi K3.1 Pro. On the unassisted reasoning benchmark HLE-Diamond, Opus 5.5 similarly leads Kimi K3.1 Pro by roughly 30 percentage points.

What hardware is required to run Kimi K3.1 Pro locally?

Running the unpruned 2.8-trillion-parameter Kimi K3.1 Pro checkpoint locally requires approximately 1.56 TB of storage and high-end hardware such as an 8x NVIDIA B300 cluster with 2.3 TB of GPU memory. For smaller setups, community pruning projects have produced experimental 700B–750B parameter variants running across fewer accelerators.

What to Watch Next

Track the rollout of community quantizations and pruned checkpoints on vLLM and SGLang, which will reveal how much reasoning fidelity Kimi K3.1 Pro retains when compressed down to enterprise-accessible clusters. Watch also for upcoming updates to the Artificial Analysis private benchmark splits to see whether closed-model vendors widen the gap on held-out tasks.

Try related models on Kie.ai: Kimi K3.1 Pro, Claude Opus 5.5 and Claude Sonnet 5.5.