GLM 5.1 vs Kimi K3.1 Pro

Z.ai · China  |  Moonshot AI · China · Updated June 2026

Quick verdict

Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). Pick Kimi K3.1 Pro for sparse moe with 2.8t total and about 104b active parameters, post-trained from the kimi k3 base or terminal-bench 4.0 of 65.7% (moonshot) and 67% as measured by artificial analysis. Choose GLM 5.1 if you need self-hosting or data privacy; Kimi K3.1 Pro if you want a managed API.

GLM 5.1 (Z.ai) and Kimi K3.1 Pro (Moonshot AI) are two of the models people most often weigh against each other in 2026. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Kimi K3.1 Pro is moonshot's October 23, 2026 post-training upgrade to Kimi K3 - a 2.8T-parameter MoE at $2/$10 with open weights promised by November 4. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGLM 5.1Kimi K3.1 Pro
ProviderZ.ai (China) Moonshot AI (China)
ReleasedApril 7, 2026 October 23, 2026
Context window200K (~300 pages) 1M tokens (~1,573 pages)
Price (in/out)$1.4/$4.4 per 1M tokens $2/$10 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Long-horizon autonomous agentic engineering (up to 8-hour runs)

GLM 5.1

An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it runs cheaper at $1.4/$4.4 per 1M tokens.

State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)

GLM 5.1

Open weights make this possible at all — Kimi K3.1 Pro is API-only, so it cannot leave the vendor's servers.

Sustained tool use across thousands of calls

GLM 5.1

Kimi K3.1 Pro is comparatively weak here — very high output-token use: roughly 150M output tokens across the Artificial Analysis index

Sparse MoE with 2.8T total and about 104B active parameters, post-trained from the Kimi K3 base

Kimi K3.1 Pro

Moonshot's October 23, 2026 post-training upgrade to Kimi K3 - a 2.8T-parameter MoE at $2/$10 with open weights promised by November 4 — and it carries the larger 1M tokens context.

Terminal-Bench 4.0 of 65.7% (Moonshot) and 67% as measured by Artificial Analysis

Kimi K3.1 Pro

Moonshot's October 23, 2026 post-training upgrade to Kimi K3 - a 2.8T-parameter MoE at $2/$10 with open weights promised by November 4 — and it is the newer of the two.

Artificial Analysis Intelligence Index of 55, second only to Claude Opus 5.5 (58) among the models AI Weekly lists

Kimi K3.1 Pro

Kimi K3.1 Pro lists artificial Analysis Intelligence Index of 55, second only to Claude Opus 5.5 (58) among the models AI Weekly lists among its strengths; GLM 5.1 does not.

Lowest cost at scale

GLM 5.1

At $1.4/$4.4 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Kimi K3.1 Pro

Its 1M tokens window is about 5.2× larger than GLM 5.1's 200K, fitting roughly 1,573 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ GLM 5.1

At $1.4/$4.4 per 1M tokens it undercuts Kimi K3.1 Pro, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Kimi K3.1 Pro

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ GLM 5.1

Open weights let you run it on your own hardware; Kimi K3.1 Pro is API-only.

Anyone whose priority is long-horizon autonomous agentic engineering (up to 8-hour runs)

→ GLM 5.1

It is specifically built for that.

Anyone whose priority is sparse moe with 2.8t total and about 104b active parameters, post-trained from the kimi k3 base

→ Kimi K3.1 Pro

That is its strongest area.

GLM 5.1: where it fits

An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.

Its trade-offs are real: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.

Kimi K3.1 Pro: where it fits

Moonshot's October 23, 2026 post-training upgrade to Kimi K3 - a 2.8T-parameter MoE at $2/$10 with open weights promised by November 4. Released October 23, 2026 by Moonshot AI, it is built for sparse MoE with 2.8T total and about 104B active parameters, post-trained from the Kimi K3 base, terminal-Bench 4.0 of 65.7% (Moonshot) and 67% as measured by Artificial Analysis, artificial Analysis Intelligence Index of 55, second only to Claude Opus 5.5 (58) among the models AI Weekly lists, and standard API price cut by a third versus K3 to $2/$10 per million tokens (Highspeed tier $4/$20).

Its trade-offs: weights promised by November 4, 2026 and not confirmed public as of November 3; license terms not yet disclosed, very high output-token use: roughly 150M output tokens across the Artificial Analysis index, self-hosting is impractical - about 1.4 TB of raw weights even at 4-bit, and most benchmarks are internal or use differing harnesses; no SWE-bench Verified figure published. At $2 in / $10 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

The defining split here is open vs. closed. GLM 5.1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Kimi K3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both GLM 5.1 and Kimi K3.1 Pro without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is GLM 5.1 or Kimi K3.1 Pro better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) while Kimi K3.1 Pro leans toward sparse moe with 2.8t total and about 104b active parameters, post-trained from the kimi k3 base, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GLM 5.1 or Kimi K3.1 Pro?

GLM 5.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Kimi K3.1 Pro is API-metered at $2/$10 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Kimi K3.1 Pro — 1M tokens vs 200K, about 5.2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both GLM 5.1 and Kimi K3.1 Pro together?

Yes — a multi-model platform like LumiChats gives you GLM 5.1, Kimi K3.1 Pro and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, GLM 5.1 or Kimi K3.1 Pro?

Kimi K3.1 Pro — released October 23, 2026, about 7 months after GLM 5.1.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.