Inkling vs Kimi K3.1 Pro
Thinking Machines Lab · US | Moonshot AI · China · Updated June 2026
Quick verdict
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. 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 Inkling if you need self-hosting or data privacy; Kimi K3.1 Pro if you want a managed API.
Inkling (Thinking Machines Lab, 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. 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. 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
- ▸Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while Kimi K3.1 Pro is API-metered at $2/$10 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: 1M vs 1M tokens — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: Kimi K3.1 Pro is the newer model by about 3 months (released October 23, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Inkling | Kimi K3.1 Pro |
|---|---|---|
| Provider | Thinking Machines Lab (US) | Moonshot AI (China) |
| Released | July 15, 2026 | October 23, 2026 |
| Context window | 1M (~1,500 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | Open weight (self-host / free) | $2/$10 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, audio, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
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 — Kimi K3.1 Pro 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
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 Kimi K3.1 Pro is API-only.
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request
Inkling
Inkling lists a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request 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 is the newer of the two.
Terminal-Bench 4.0 of 65.7% (Moonshot) and 67% as measured by Artificial Analysis
Kimi K3.1 Pro
Inkling is comparatively weak here — independent third-party benchmark verification is still limited given how recently it shipped
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; Inkling does not.
Lowest cost at scale
Inkling
Its weights are open, so at volume you pay for your own hardware instead of Kimi K3.1 Pro's $2/$10 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
At Open weight (self-host / free) 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
→ Inkling
Open weights let you run it on your own hardware; Kimi K3.1 Pro is API-only.
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
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
→ Inkling 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.
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 are real: 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.
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. Inkling 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 Inkling 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 pricingFrequently asked questions
Is Inkling 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, 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 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, Inkling or Kimi K3.1 Pro?
Inkling 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?
Effectively neither — 1M vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Inkling and Kimi K3.1 Pro together?
Yes — a multi-model platform like LumiChats gives you Inkling, 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, Inkling or Kimi K3.1 Pro?
Kimi K3.1 Pro — released October 23, 2026, about 3 months 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.