Around October 23–24, 2026 (with Kimi Code documentation noting an October 23 launch and media outlets widely reporting the release on October 24), Moonshot AI officially introduced Kimi K3.1 Pro, the inaugural model in the K3.1 series. Positioned by the company as its most capable "3-trillion-class" parameter model to date, K3.1 Pro supports a 1-million-token context window, launches alongside a high-throughput variant, and comes with an official commitment to release full model weights in the near future.
Rather than a routine conversational update, K3.1 Pro represents a focused bet on "getting things done"—specifically targeting long-horizon agent capabilities across software engineering, 3D creation, and financial research. Just as notably for developers and technical decision-makers, these performance gains arrive alongside a significant reduction in API pricing compared to its predecessor.
1. Version Positioning and Technical Foundation: A Large-Scale Post-Trained Evolution of K3
To evaluate Kimi K3.1 Pro accurately, it is important to clarify its evolutionary relationship with the previous generation. Rather than a completely new base architecture built from scratch independent of K3, K3.1 Pro is a post-trained version of Kimi K3 backed by a massive increase in post-training compute.
1.1 Product Matrix and Version Evolution
As the first model in the K3.1 lineup, K3.1 Pro carries the official designation of Moonshot AI's most capable 3-trillion-class parameter model and supports a 1-million-token context window (with comparative technical analyses specifying the window at 1,048,576 tokens). Moonshot AI has not disclosed an exact parameter count; third-party analyses infer that the base architecture likely retains K3's 2.8-trillion parameter foundation, though this remains unconfirmed by official documentation.
To serve different latency requirements, Moonshot AI launched a dual-version strategy:
- Kimi K3.1 Pro (Standard): The flagship model for complex reasoning and long-horizon execution.
- Kimi K3.1 Pro Highspeed: A high-speed variant delivering equivalent intelligence at approximately 2x to 3x the output speed.
Furthermore, K3.1 Pro is positioned as an open-weight model. Moonshot AI announced that the full model weights will be released before November 4, 2026. The initial launch announcement did not specify the licensing terms, which will need to be verified via the official model card once the weights go live.
1.2 Full-Stack Optimization and Large-Scale Reinforcement Learning (RL)
The capability leap from K3 to K3.1 Pro is driven by intensive post-training engineering. To support large-scale joint training across hundreds of downstream domains, Moonshot AI refactored and optimized every layer of its technical stack. According to official disclosures, these upgrades span the reinforcement learning (RL) framework, long-context inference, training efficiency, sandbox infrastructure, scheduling systems, and a shared platform dedicated to environment quality inspection.
By eliminating bottlenecks across these layers, the newly added RL compute translated into stable, high-efficiency returns, yielding broad improvements across all evaluated domains.
Note on Architecture and Multimodality (Third-Party Reviewer Perspective): According to independent personal reviews—rather than official parameter sheets—K3.1 Pro utilizes a Mixture-of-Experts (MoE) architecture where the 3-trillion figure represents total parameters while only a subset is activated per token, keeping operational costs lower than dense models of comparable scale. Third-party reviewers also note that the model is natively multimodal with image understanding capabilities.
2. Core Capability Upgrades: From "Answering Questions" to "Getting Things Done"
Kimi K3.1 Pro's primary breakthroughs lie in its ability to execute multi-step workflows, invoke external tools, and deliver end-to-end results across coding, 3D modeling, and financial analysis, while simultaneously curbing factual hallucinations.
2.1 Software Engineering and Long-Horizon Agents: Approaching Top Proprietary Models
According to Moonshot AI, K3.1 Pro "reaches or approaches the level of the strongest closed-source models" across coding, agentic, and knowledge benchmarks.
- Terminal-Bench 4.0: K3.1 Pro scored 65.7%, surpassing GPT-6 Astra (58.2%) and trailing Claude Opus 5.5 (66.4%) by only a narrow margin.
- Kimi Code Bench 3.0 Long-horizon (Internal Benchmark): K3.1 Pro achieved 61.2%, up from K3's 55.95%. This internal evaluation covers diverse engineering domains and specifically tests multi-step execution, tool invocation, and end-to-end problem-solving.
In practical engineering workflows, the model's scope ranges from locating and fixing bugs within large, real-world codebases to building complete projects from scratch. It covers frontend development, interactive web experiences, and game development, demonstrating the ability to autonomously select and invoke appropriate external professional tools.
According to media reports showcasing application cases, K3.1 Pro can invoke professional engines and software such as Unity, Unreal Engine, Godot, and Blender. Reported media examples include using Unreal Engine 5.8 to assemble game scenes, writing scripts in Blender for procedural modeling, and building a 3D action-adventure game from scratch in Godot 4.7.2.
2.2 3D Creation and "3D Make": Bridging Modeling and 3D Printing
One of K3.1 Pro's most differentiated upgrades is its spatial reasoning and tool-assisted 3D creation workflow.
- BenchCAD Benchmark: With tool assistance, K3.1 Pro achieved a score of 92.7%, coming close to Claude Opus 5.5 (96.2%) and GPT-6 Astra (95.9%).
- Technical Mechanism: Given text descriptions, sketches, or reference images, the model infers object shapes, proportions, and spatial relationships. It then invokes professional software such as Blender and FreeCAD, writing and executing scripts to reconstruct usable 3D models.
- Product Integration ("3D Make"): Alongside the model release, Moonshot AI launched the 3D Make feature on both the web portal (
kimi.com) and the Kimi App. It supports online previewing, editing, and exporting for 3D printing, covering three distinct creation categories: 3D Web, 3D Modeling, and 3D Printing.
2.3 Financial Research and Complex Business Analysis: Setting a New SoTA
For enterprise and analytical workflows, K3.1 Pro demonstrates major gains in document retrieval, cross-document verification, and multi-step numerical reasoning.
- OfficeQA Pro v2: K3.1 Pro scored 64.4, which Moonshot AI states is the current State-of-the-Art (SoTA). This benchmark evaluates complex financial data analysis grounded in real-world financial documents.
- Internal Financial Benchmarks: Compared to Kimi K3, K3.1 Pro improved by 14% in financial modeling and 20% in financial research, while its business operations score reached approximately 4.2x that of K3.
- End-to-End Delivery: Moonshot AI states that the model can deliver complete financial workflows end-to-end and achieves further gains over K3 on the Frontier Finance evaluation.
2.4 Factual Accuracy and Hallucination Control
To evaluate reliability under adversarial or tricky conditions, Moonshot AI tested the model on its internal stress test, Kimi Hallu Bench Hard. Because different official and analytical channels report slightly different figures depending on the specific tier or language edition, both sets of statistics are noted below:
- English Blog & Media Reporting Standard: The error rate on individual factual statements dropped from 23.4% (in K3) to 8.3%, and the proportion of responses containing at least one factual error was reduced to approximately 42.4% of the previous generation's rate.
- Alternative Chinese Comparative Reporting Standard: Another comparative analysis cites the individual factual statement error rate dropping from 23.35% to 6.85%, with responses containing at least one error at 46.4% of the prior generation's level.
Moonshot AI explicitly notes that Kimi Hallu Bench Hard intentionally targets edge cases where models are most prone to mistakes; therefore, these reported error rates do not represent overall error rates in everyday usage.
3. Horizontal Comparison: Where Does K3.1 Pro Stand in the Current Landscape?
To place Kimi K3.1 Pro in perspective, it is helpful to compare it both vertically against Kimi K3 and horizontally against leading international proprietary flagships—specifically GPT-6 Astra and Claude Opus 5.5.
3.1 Vertical Comparison with Kimi K3: Higher Capability at Lower Cost
Both Kimi K3 and K3.1 Pro support a 1-million-token context window. The core value of this iteration is not context expansion, but rather a comprehensive leap in task completion quality—such as a 5.25 percentage point gain on Kimi Code Bench 3.0 Long-horizon, up to 4.2x gains in internal business/finance metrics, and significantly lower hallucination rates—paired with a substantial drop in API pricing.
Measured per 1 million tokens in RMB, K3.1 Pro Standard cuts costs across the board compared to K3:
- Kimi K3.1 Pro Standard: Input ¥13 / Output ¥68 / Cache Hit ¥1.30
- Kimi K3: Input ¥20 / Output ¥100 / Cache Hit ¥2.00
3.2 Horizontal Comparison with GPT-6 Astra and Claude Opus 5.5
On capability benchmarks, K3.1 Pro trades blows with top proprietary models depending on the domain. In Terminal-Bench 4.0 (65.7%), it outperforms GPT-6 Astra (58.2%) and nearly matches Claude Opus 5.5 (66.4%). In BenchCAD (92.7%), it trails slightly behind both Opus 5.5 (96.2%) and Astra (95.9%).
Benchmark Objectivity Note: Readers and technical evaluators should keep in mind that several benchmarks cited above (such as Kimi Code Bench and the internal financial suite) are vendor-internal evaluations with limited external reproducibility. Furthermore, competitor scores are largely sourced from those competitors' own official blogs or system cards, meaning the evaluation harnesses are not strictly identical. These figures should not be treated as independent, third-party head-to-head retests (full official benchmark details are published at kimi.com/blog/kimi-k3-1-pro).
Where K3.1 Pro establishes a massive gap is in API pricing. Because different sources report pricing in RMB versus USD with slightly different conversion assumptions, both standards are preserved below:
| Model / Variant | Input (per 1M tokens) | Output (per 1M tokens) | Cache Hit (per 1M tokens) | Source / Currency Standard |
|---|---|---|---|---|
| Kimi K3.1 Pro (Standard) | ¥13 (~$2) | ¥68 (~$10) | ¥1.30 | Official RMB table; USD figures per DataCamp / 3rd-party reviews |
| Kimi K3.1 Pro Highspeed | ¥26 (~$4) | ¥136 (~$20) | ¥1.30 | Official RMB table (2x Standard); USD figures per DataCamp |
| GPT-6 Astra | ¥67.18 ($10) | ¥335.90 ($50) | ¥6.72 ($1) | Comparative pricing table |
| Claude Opus 5.5 | ¥26.87 ($4) | ¥134.36 ($20) | ¥1.34 ($0.20) | Comparative pricing table |
As highlighted by third-party reviewers using the USD pricing standard ($2 input / $10 output), K3.1 Pro Standard's output token price is roughly one-fifth that of GPT-6 Astra.
Real-World Execution Speed and Agent Behavior: While K3.1 Pro is highly competitive on accuracy and cost, real-world latency varies by task profile. According to a hands-on personal review by an independent developer testing an identical complex task, GPT-6 Astra completed the job in about 2–3 minutes, whereas Kimi K3.1 Pro took roughly 10 minutes. However, the reviewer noted that during those 10 minutes, Kimi proactively performed self-checks, adjusted lighting parameters, switched ports, and adapted the interface for mobile screens—demonstrating thorough agentic self-reflection. Separately, tech media headlines have noted early community chatter stating that while the model is faster than K3 and approaches Opus in performance, complex multi-step agent loops can consume user quotas rapidly (with one headline quipping that quotas burned out in 10 minutes, though the report text did not provide specific token consumption telemetry).
4. Access Channels, Pricing Rules, and Open-Weight Timeline
Kimi K3.1 Pro is immediately available across consumer, developer, and enterprise touchpoints, with specific tier rules and lifecycle milestones to note.
4.1 Consumer and Developer Access Channels
Both Kimi K3.1 Pro and K3.1 Pro Highspeed are live across official channels:
- Web & Mobile:
kimi.comand the Kimi App - Productivity & Coding Suites: Kimi Work and Kimi Code
- Developer Platforms: Kimi API (
platform.kimi.com), as well as third-party aggregation platforms such as OpenRouter (per DataCamp documentation).
Within Kimi Code, access is tiered by membership level:
- K3.1 Pro Standard: Available to Plus / Moderato tiers and above; unlocking the full 1M token context window requires Pro / Allegretto or higher.
- K3.1 Pro Highspeed: Requires Pro / Allegretto membership or above.
4.2 API Rules, Privacy, and Enterprise Configuration
For developers integrating via platform.kimi.com:
- Pricing: Standard edition is billed at ¥13 per 1M input tokens (cache miss), ¥1.30 per 1M input tokens (cache hit), and ¥68 per 1M output tokens. The Highspeed edition doubles the input and output rates (¥26 input / ¥136 output) while keeping cache hits at ¥1.30.
- Rate Limit Unlocking: According to DataCamp's developer guide, API accounts unlock access to K3.1 Pro after topping up a minimum of $1 USD, with cumulative top-up amounts determining the account's rate limit tiers.
- Data Privacy (ZDR): API customers have the option to enable Zero Data Retention.
- Kimi Enterprise: For enterprise organizations, K3.1 Pro is disabled by default upon release; Kimi Enterprise administrators must manually enable the model for their organization in the admin console.
4.3 Open-Weight Schedule and K2 Series Deprecation Notice
- Weight Release: Full model weights for K3.1 Pro are scheduled for public release before November 4, 2026. Teams planning self-hosted deployments should check the official model card upon release to confirm the exact open-source license terms.
- K2 Series Retirement: According to third-party documentation referencing Moonshot AI's developer docs, the legacy K2 model series will no longer be offered to new users starting October 31, 2026, and is scheduled for full retirement by the end of December 2026. Existing K2 API users should begin planning their migration paths immediately.
5. Use-Case Matching and Migration Guide: Should You Switch Now?
With K3.1 Pro offering superior benchmark numbers and lower API rates than K3, migrating seems straightforward—but engineering teams should align their expectations with their specific workload profiles.
5.1 Matching Workloads to Model Variants
- High-ROI Scenarios: K3.1 Pro shines brightest in heavy, multi-step workflows: cross-file bug localization and repair in large codebases, building full-stack or game projects from scratch, 3D asset generation and printable modeling, deep financial report synthesis, and long-horizon agent execution.
- Low-Perception Scenarios: If your daily workload consists primarily of short Q&A or simple, single-turn text generation, the heavy post-training upgrades in K3.1 Pro may not feel noticeably different from K3.
- Standard vs. Highspeed Selection Rule: Default to the Standard edition. The Highspeed variant does not carry separate capability benchmark scores; its sole advantage is a 2x–3x speed boost at double the input and output token cost. Consequently, Highspeed is only cost-effective in interactive scenarios where a human user is actively waiting in real time for a response.
5.2 Best Practices for Developer Evaluation and Migration
As industry analysts point out, the best way to evaluate a long-horizon model like K3.1 Pro is not to "ask it a harder trivia question," but to hand it a task with a clear finish line. The longer the execution chain, the more essential it becomes to pair the model with clear acceptance criteria, reliable toolchains, and human review gates.
When migrating production workflows from Kimi K3 to K3.1 Pro, engineering teams should follow a disciplined A/B testing protocol:
- Control the Variables: Run K3 and K3.1 Pro against the exact same inputs, in independent sessions, equipped with the identical set of external tools and validation checks.
- Inspect the Diff, Not Just the Explanation: When evaluating code generation or bug fixes, do not judge the model solely by the fluency of its final summary. Verify whether the code modifications stayed strictly within the expected scope, whether unit tests properly cover the fixed logic, and whether any silent regressions were introduced.
- Stage the Rollout: Start by migrating a single, repeatable workflow. Keep your previous K3 prompt/tool configuration intact alongside a small regression test suite so your team can roll back instantly if unexpected behaviors emerge in production.
Conclusion
The release of Kimi K3.1 Pro signals a clear shift in the frontier LLM race. Rather than competing solely on base pre-training parameter counts or raw context window length, the focus has moved decisively toward large-scale post-training (RL), specialized tool invocation (from compilers to 3D engines), end-to-end task completion rates, and inference cost efficiency.
By matching or approaching top proprietary models like Claude Opus 5.5 and GPT-6 Astra on key coding and agentic benchmarks—while cutting API prices below its own predecessor—Moonshot AI has delivered a compelling upgrade for developers and enterprises alike. With full model weights slated to drop before November 4, K3.1 Pro is poised to become a pivotal release for both the commercial API market and the global open-weight ecosystem.
