Kimi K3 vs Grok 4.5: Open-Weight Scale vs xAI's Agentic Model

Kimi K3 and Grok 4.5 both launched in July 2026 aimed at coding and agentic work, but they take opposite approaches to access. Kimi K3, from Moonshot AI, is a roughly 2.8-trillion-parameter Mixture-of-Experts model released July 16, 2026, with a 1-million-token context window and open weights published on Hugging Face. Grok 4.5, from xAI, launched July 8, 2026 as a closed, hosted model built for coding, agentic tasks, and knowledge work.

Grok 4.5 costs less per token. Kimi K3 API pricing is $3 input ($0.30 on cache hits) and $15 output per million tokens, while Grok 4.5 is $2 input ($0.30 on cache hits) and $6 output per million tokens for contexts up to 200k.

The bigger split is infrastructure and verification. Kimi K3 hands you frontier-scale weights to self-host, at the cost of heavy GPU infrastructure and a data-residency review, since Moonshot is a China-based lab. Grok 4.5 hands you a hosted model with enterprise integrations, but its compliance certifications were not detailed in the public materials reviewed for this comparison.

Kimi K3 vs. Grok 4.5: Side-by-Side

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Kimi K3 vs Grok 4.5: The Quick Verdict

Neither model has a fully settled story yet, so this is a pilot-both decision for most SMBs. Grok 4.5 is the lower-effort start if you are already on Databricks or Amazon Bedrock and want a hosted agentic model with no infrastructure to run. Kimi K3 is the pick when you need full data control through self-hosting or want frontier-scale open weights, and you can fund the hardware.

Two honest caveats apply. Kimi K3's benchmark numbers are self-reported by Moonshot, and the published open weights now let third parties check them. Grok 4.5's compliance certifications were not detailed in the public materials reviewed for this comparison, so confirm them directly with xAI before you sign.

Capabilities and Performance

Kimi K3 is built for long-horizon coding and agentic work at frontier scale, with a 1-million-token context window, native vision, and an always-on reasoning mode Moonshot calls "thinking mode." Moonshot positions it as its most capable model to date and the largest open-weight model yet released.

Grok 4.5 is xAI's release for coding, agentic tasks, and knowledge work, with integrations built for platforms like Databricks and Amazon Bedrock. xAI positions it for technical, multi-step business workflows rather than general content generation.

Kimi K3 vs Grok 4.5: The Benchmark Numbers

Treat both models' performance claims with care until more independent data exists. Kimi K3's benchmark numbers are self-reported by Moonshot at its July 16, 2026 launch and are not independently verified, although the open weights that let third parties check them are now published.

On its own evaluation suite, Moonshot reports Kimi K3 at 67.5 on DeepSWE, 77.8 on ProgramBench, 88.3 on Terminal-Bench 2.1, and 42.0 on SWE Marathon, plus a state-of-the-art 91.2 on BrowseComp for long-horizon information seeking. xAI has not published Grok 4.5 results on these specific benchmarks, so there is no like-for-like number to place beside them.

The one independent, like-for-like signal available today is LMArena's Frontend Code Arena, a blind human-preference test, where Kimi K3 ranked first at 1,679 points at launch. That is a genuine third-party data point for front-end coding, though it does not cover Grok 4.5's broader agentic claims.

For buying guidance: run your own pilot on both. Neither vendor has published a fully independent, like-for-like benchmark against the other, so your own workload is the most reliable test available today.

Compliance Posture for Regulated Business

Grok 4.5's certification status was not detailed in the public materials reviewed for this comparison. Confirm SOC 2 status, HIPAA BAA availability, and data-retention terms directly with xAI before sending any regulated data.

Kimi K3 is built by a China-based lab, so its hosted API's data location and retention terms need review before you send sensitive data. Self-hosting the open weights removes that concern, since no request leaves your own infrastructure, but you then own the full compliance burden.

• Grok 4.5: certification status not detailed publicly; confirm directly with xAI.
• Kimi K3 hosted API: review data location and retention terms before sending sensitive data.
• Kimi K3 self-hosted: full data control, but you own the compliance work.

Cost and Total Cost of Ownership

Kimi K3 costs $3 input ($0.30 on cache hits) and $15 output per million tokens on the hosted API, or nothing per token to self-host beyond hardware costs. Grok 4.5 costs $2 input ($0.30 on cache hits) and $6 output per million tokens for contexts up to 200k, so a job that produces 1M output tokens costs $6 on Grok 4.5 and $15 on Kimi K3's hosted API.

Self-hosting Kimi K3 removes per-token fees, but a 2.8-trillion-parameter model demands very heavy GPU capacity, engineering time, and ongoing maintenance. That hardware bill is the line item that most often surprises teams comparing the two on sticker price alone.

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Best Fit by Use Case

Choose Grok 4.5 when you are already building on Databricks or Amazon Bedrock and want a hosted, agentic model with no infrastructure to run — after you confirm its compliance terms directly with xAI.

Choose Kimi K3 when you need full data control through self-hosting, want frontier-scale open weights at a published price, or run high enough volume that per-token savings outweigh the heavy infrastructure cost.

How to Use Kimi K3 and Grok 4.5

To use Kimi K3 and Grok 4.5 effectively, start by choosing an interface that exposes the exact model you need for your workload. Test a new configuration on a single representative task, such as drafting a routine email or parsing a short document, before expanding to full automation. Inspect the saved output directly to confirm formatting, accuracy, and adherence to your prompt instructions.

For coding tasks, Cursor serves as an editor option when the target model appears on its supported list. Check current model availability before setup to confirm the exact model is listed. Once connected, save your prompts and review the generated diffs locally before applying changes to your main codebase.

The Verdict

Grok 4.5 is the lower-effort start if you already run on Databricks or Amazon Bedrock — but confirm its compliance certifications directly with xAI, since they were not detailed in the public materials reviewed for this comparison.

Kimi K3 wins on open weights and frontier scale, and is worth a pilot now that its weights are published so you can confirm Moonshot's claims on your own workload.

If your data is regulated, plan to self-host Kimi K3 or get written compliance terms from xAI for Grok 4.5 before you send any sensitive data to either hosted API.