--- title: "Kimi K3.1 Pro vs Opus 5.5 vs GPT 6.1 Soul: Which Is Better?" url: "https://goremy.ai/blog/articles/kimi-k3-1-pro-vs-opus-5-5-gpt-6-1-soul" author: Luis Chavez-Mattos editor: Luis Chavez-Mattos editor_title: Director, Product published: "2026-10-25T17:20:07.133Z" updated: "2026-10-27T05:00:16.472Z" topic: Open-Weight Model Benchmarks description: "Kimi K3.1 Pro tested against Opus 5.5 and GPT 6.1 Soul: pricing, open-weight caveats, and hands-on demos from a Rubik's cube to a Fall Guys clone." --- # Kimi K3.1 Pro vs Opus 5.5 vs GPT 6.1 Soul: Which Is Better? > Kimi K3.1 Pro tested against Opus 5.5 and GPT 6.1 Soul: pricing, open-weight caveats, and hands-on demos from a Rubik's cube to a Fall Guys clone. Edited by Luis Chavez-Mattos, Director, Product · Published 2026-10-25 · Updated 2026-10-27 ## Is Kimi K3.1 Pro competitive with Claude Opus 5.5 and GPT 6.1 Soul? Mostly yes, with caveats. Moonshot's launch benchmarks show Kimi K3.1 Pro [trading blows with the top closed models](https://goremy.ai/blog/articles/kimi-k3-1-pro-vs-opus-5-5-open-source-caught-up), and hands-on demos back that up in some areas, especially 3D work. It falls short in others, including precise spatial reasoning, polish, and visual taste. In side-by-side tests it needed follow-up prompts more often than the closed leaders did. For an open-weights model of roughly 3 trillion parameters, the showing is strong. ## TL;DR - Kimi K3.1 Pro is a **3 trillion parameter class open-weights model**, up from 2.8 trillion for its predecessor, with full weights promised for November 4. - The **open-weights release will be weaker at cyber tasks** than the API version, because Moonshot removed cyber-boosting material during training while keeping general coding ability. - API pricing is **$2 per million input tokens and $10 per million output tokens**, and a high-speed variant costs double ($4 and $20). - Its best demo was a **3D Rubik's cube with a real solver**, a test open-weights models have historically failed. - It struggled with **Blender knot modeling and rope motion graphics**, where GPT 6.1 Soul produced a clearly better result. - Early reports describe **usage limits draining fast** on subscription plans, so check that before committing. ## What is Kimi K3.1 Pro? Kimi K3.1 Pro is Moonshot's newest flagship model. It is listed as a 3 trillion parameter class open-weights model. Its predecessor, Kimi K3, sat at 2.8 trillion parameters. Moonshot says it will release the full weights on November 4, so exact specs should be confirmed then. There is also a Kimi K3.1 Pro high-speed variant that Moonshot says is two to three times faster for more responsive interactions. It costs more. The launch table compares it against closed frontier models and against DeepSeek V4.1 Flash. That last model is much smaller, and it trails on every benchmark. The comparison reads partly as a flattering reference point for the smaller model's price class. One gap in the table: it does not show Kimi K3, so you can't see the generational jump directly. ## How do the published benchmarks compare? The launch chart shows K3.1 Pro competing with the best closed models and, by one reviewer's reading, ahead of an earlier Fable release. This article does not quote individual scores for Terminal Bench, GDPval or OSWorld, because the source material here doesn't include the underlying numbers. Pull exact figures from Moonshot's launch page. Two reasonable ways to read the chart: - Closed models are probably much larger than a 3T open model, so matching them on several rows is notable. - Benchmark tables are vendor-selected. The reviewers behind these tests explicitly worried about "benchmaxing" and judged the model by its outputs instead. ## How much does Kimi K3.1 Pro cost? | Variant | Input (per 1M tokens) | Output (per 1M tokens) | |---|---|---| | Kimi K3.1 Pro | $2 | $10 | | Kimi K3.1 Pro high-speed | $4 | $20 | At standard rates it competes on price with Gemini 4 Argon, Sonnet 55 and GPT 6.1 Soul. One reviewer noted that Argon is expected to become about twice as expensive within a month or two, which would shift that comparison. Subscription plans are a separate question. Users on X reported that five-hour usage limits vanished almost instantly, particularly in fast mode. Neither reviewer could verify plan pricing or limits at launch, and a similar limit bug on another model launch was later fixed. Treat it as something to test yourself. ## What is the catch with the open weights? Moonshot says it identified and removed material that boosted cyber-related capability from training for the open-weights version, while keeping general-purpose coding. The API model keeps the stronger cyber behavior. If your work touches security tooling, the downloadable model and the hosted one will not be equivalent. Some users were upset about this. Whether it matters depends on your use case. ## Where does Kimi K3.1 Pro win? **3D rendering and animation.** Moonshot says this is a strength, and the Rubik's cube test supports it. Open-weights models have tended to fail this prompt: colors vanish, rotations break, or the cube suddenly flips to solved. K3.1 Pro produced a convincing 3D cube with reflections that shift as you rotate it. It also had keyboard controls, a timer, undo, reset, adjustable turn speed and a scramble button. Its solver worked out an actual solution and played it back move by move, ending in a confetti animation. The reviewer called it one of the best results for this test. **Competent game mechanics.** A self-contained C++ skateboarding game, set on an early 2000s New York block with a Z-key replay feature, worked without inverted controls. It had pedestrian logic and a fire hydrant. The graphics were simple, though. A follow-up prompt improved the visuals, but it also inverted forward and backward controls. **Long agentic builds.** A Fall Guys clone ("Bean Blitz") took 48 minutes and came back with 12 animated competitors, CPU rivals, rotating sweepers, sliding gates, bouncing pads, swinging balls, checkpoints, sound effects, touch controls and adjustable settings. Colors were too pastel, collisions were inaccurate and there was flickering, but it was a decent result for an open-weights model. ## Where does it fall short? - **Browser OS.** The single-file "Lumen" desktop had working sound, search, notes with save to txt, a file browser and games. It had no right-click menu, few apps, and wallpaper changes that left icons unchanged. The foundation was acceptable and the implementation disappointing. A built-in GTA-style game improved after a complaint but the main issue stayed. - **Accuracy to real objects.** A WebGL iPhone Duo render looked good technically, with metallic reflections, but did not match the device. The Apple logo looked off and the split-screen design was missing. The model searched the web and the render still took 28 minutes 45 seconds. A follow-up asking it to research real specs was a nudge to improve accuracy. - **Spatial reasoning with tools.** Asked to model a bowline knot in Blender, it used Blender and searched the web, but the knot was not tied correctly, was not attached to the harness, and the human figure looked odd. The reviewer called it a failure. - **Instructional motion graphics.** The first rope animation did not show overlaps clearly, so it could not teach the knot. A "make it prettier" follow-up added shadow and camera movement without fixing that. GPT 6.1 Soul on medium effort made a 54-second version where the rope's over and under crossings were clear enough to follow. - **Overcaution.** The motion graphics carried safety disclaimers, similar to behavior seen in ChatGPT and Claude models. ## How does it behave in practice? Short prompts work fine. The reviewers noted that keeping prompts brief shows the model's own taste, while strong preferences about colors, tech stack or level design call for more detail. Expect to iterate: several tests improved noticeably only after a critique. Design output tended toward a familiar AI look, with the website redesign following brand colors and fonts but showing spacing errors. One reviewer saw Claude-style "eyebrow text" in its interfaces. It also built a five-minute video parody by orchestrating AI video generation, Python, NumPy audio and FFmpeg, which shows how far its tool use extends. ## Which model should you pick? | If you need | Lean toward | |---|---| | Open weights, self-hosting, 3D and interactive web demos | Kimi K3.1 Pro | | Clear step-by-step visual explanations, as in the rope test | GPT 6.1 Soul | | Top visual polish in generated games | Anthropic models, per one reviewer | | Cheap first-pass coding | DeepSeek V4.1 Flash | This is based on a handful of creative tests, not a controlled evaluation. ## Frequently Asked Questions ### Is Kimi K3.1 Pro open source? It is open-weights. Moonshot says full weights arrive November 4. The public release has reduced cyber capability compared with the API model. ### How much does Kimi K3.1 Pro cost compared to Opus 5.5? Kimi K3.1 Pro is $2 per million input and $10 per million output tokens. The high-speed variant is $4 and $20. The sources here don't give Opus 5.5 pricing, but Kimi's standard rate puts it in the same bracket as Gemini 4 Argon, Sonnet 55 and GPT 6.1 Soul. ### Is Kimi K3.1 Pro good at coding? Moonshot says general-purpose coding was kept in the open-weights version. Demos produced working games and apps, though polish and accuracy varied and follow-up prompts helped. ### Should I worry about usage limits? Early reports on X described subscription limits draining quickly, especially in fast mode. The reviewers tested through an API endpoint and could not confirm the plan behavior, so check it with your own usage.