GPT-6 Astra vs Inkling

OpenAI · US  |  Thinking Machines Lab · US · Updated June 2026

Quick verdict

Pick GPT-6 Astra for computer & browser use (screenspot-pro 92.7%) or cybersecurity exploit development (exploitbench 100%). Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. Choose Inkling if you need self-hosting or data privacy; GPT-6 Astra if you want a managed API.

GPT-6 Astra (OpenAI) and Inkling (Thinking Machines Lab) are two of the models people most often weigh against each other in 2026. 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. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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

SpecGPT-6 AstraInkling
ProviderOpenAI (US) Thinking Machines Lab (US)
ReleasedSeptember 3, 2026 July 15, 2026
Context window1.05M tokens (~1,575 pages) 1M (~1,500 pages)
Price (in/out)$10/$50 per 1M tokens Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, image text, image, audio, 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

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 — and it is the newer of the two.

Cybersecurity exploit development (ExploitBench 100%)

GPT-6 Astra

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

The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI

Inkling

Open weights make this possible at all — GPT-6 Astra is API-only, so it cannot leave the vendor's servers.

A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model

Inkling

GPT-6 Astra is comparatively weak here — no native audio or video input

A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request

Inkling

Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and its weights are open while GPT-6 Astra is API-only.

Lowest cost at scale

Inkling

Its weights are open, so at volume you pay for your own hardware instead of GPT-6 Astra's $10/$50 per 1M tokens.

Which should you pick?

A cost-sensitive startup shipping high volume

Inkling

At Open weight (self-host / free) 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.

A team with data-privacy or self-hosting needs

Inkling

Open weights let you run it on your own hardware; GPT-6 Astra is API-only.

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

GPT-6 Astra

It is specifically built for that.

Anyone whose priority is the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai

Inkling

That is its strongest area.

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.

Inkling: where it fits

Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.

Its trade-offs: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

The defining split here is open vs. closed. Inkling gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Astra 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 GPT-6 Astra and Inkling 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 Inkling 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 Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GPT-6 Astra or Inkling?

Inkling is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Astra is API-metered at $10/$50 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?

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

Can I use both GPT-6 Astra and Inkling together?

Yes — a multi-model platform like LumiChats gives you GPT-6 Astra, Inkling 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 Inkling?

GPT-6 Astra — released September 3, 2026, about 50 days after Inkling.

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.