GPT-6 Astra vs Kimi K3.1 Pro

OpenAI · US  |  Moonshot AI · China · Updated June 2026

Quick verdict

Pick GPT-6 Astra for computer & browser use (screenspot-pro 92.7%) or cybersecurity exploit development (exploitbench 100%). 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. On a tight budget at scale, Kimi K3.1 Pro is the value pick.

GPT-6 Astra (OpenAI, US) and Kimi K3.1 Pro (Moonshot AI, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-6 Astra is openAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. 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 and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGPT-6 AstraKimi K3.1 Pro
ProviderOpenAI (US) Moonshot AI (China)
ReleasedSeptember 3, 2026 October 23, 2026
Context window1.05M tokens (~1,575 pages) 1M tokens (~1,573 pages)
Price (in/out)$10/$50 per 1M tokens $2/$10 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1M96.3% Not published

Who wins what

Computer & browser use (ScreenSpot-Pro 92.7%)

GPT-6 Astra

GPT-6 Astra lists computer & browser use (ScreenSpot-Pro 92.7%) among its strengths; Kimi K3.1 Pro does not.

Cybersecurity exploit development (ExploitBench 100%)

GPT-6 Astra

GPT-6 Astra lists cybersecurity exploit development (ExploitBench 100%) among its strengths; Kimi K3.1 Pro does not.

Frontier math reasoning (FrontierMath Tier 4: 97.6%)

GPT-6 Astra

GPT-6 Astra lists frontier math reasoning (FrontierMath Tier 4: 97.6%) among its strengths; Kimi K3.1 Pro does not.

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 runs cheaper at $2/$10 per 1M tokens.

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; GPT-6 Astra does not.

Lowest cost at scale

Kimi K3.1 Pro

At $2/$10 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Kimi K3.1 Pro

At $2/$10 per 1M tokens it undercuts GPT-6 Astra, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ GPT-6 Astra

Larger 1.05M tokens window fits more in one prompt.

Anyone whose priority is computer & browser use (screenspot-pro 92.7%)

→ GPT-6 Astra

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.

An enterprise with regional data-residency rules

→ GPT-6 Astra or Kimi K3.1 Pro

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

GPT-6 Astra: where it fits

OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. Released September 3, 2026 by OpenAI, it is built for computer & browser use (ScreenSpot-Pro 92.7%), cybersecurity exploit development (ExploitBench 100%), frontier math reasoning (FrontierMath Tier 4: 97.6%), and long-context recall (MRCR v2 512K-1M: 96.3%).

Its trade-offs are real: no native audio or video input, pricing doubles for prompts over 272K tokens (input/cache 2x, output 1.5x), trails Meta's Muse Spark 1.3 on some coding evals (DeepSWE v1.1: 74.1 vs 75.4), and a separate opt-in "Daybreak" program gives vetted cybersecurity defenders a less-restricted version for legitimate vulnerability research; the public version already refuses ~91.5% of offensive cyber jailbreak attempts by default. At $10 in / $50 out per million tokens, it sits in the premium 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

This is less "which is smarter" and more "which ecosystem fits." GPT-6 Astra (US) and Kimi K3.1 Pro (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Kimi K3.1 Pro is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both GPT-6 Astra 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 GPT-6 Astra 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, GPT-6 Astra leans toward computer & browser use (screenspot-pro 92.7%) 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, GPT-6 Astra or Kimi K3.1 Pro?

Kimi K3.1 Pro is cheaper — $10/$50 per 1M tokens vs $2/$10 per 1M tokens, roughly 5× apart on input.

Which has the bigger context window?

Effectively neither — 1.05M tokens vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both GPT-6 Astra and Kimi K3.1 Pro together?

Yes — a multi-model platform like LumiChats gives you GPT-6 Astra, 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, GPT-6 Astra or Kimi K3.1 Pro?

Kimi K3.1 Pro — released October 23, 2026, about 50 days after GPT-6 Astra.

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.