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 Qwen 3.6 Plus for strong gpqa diamond science reasoning or open-weight and budget-friendly. Choose Inkling if you need self-hosting or data privacy; Qwen 3.6 Plus if you want a managed API.
Inkling (Thinking Machines Lab, US) and Qwen 3.6 Plus (Alibaba, 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. Qwen 3.6 Plus is alibaba's open-weight contender — surprising benchmark wins at a budget price. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while Qwen 3.6 Plus is API-metered at $0.325/$1.95 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Inkling is the newer model by about 3 months (released July 15, 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.
Specifications
Spec
Inkling
Qwen 3.6 Plus
Provider
Thinking Machines Lab (US)
Alibaba (China)
Released
July 15, 2026
April 2, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$0.325/$1.95 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, audio, code
text, image, code
SWE-Bench Verified
Not published
78.8%
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 — Qwen 3.6 Plus 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 Qwen 3.6 Plus is API-only.
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 it is the newer of the two.
Strong GPQA Diamond science reasoning: Qwen 3.6 Plus — Qwen 3.6 Plus lists strong GPQA Diamond science reasoning among its strengths; Inkling does not.
Open-weight and budget-friendly: Qwen 3.6 Plus — Qwen 3.6 Plus lists open-weight and budget-friendly among its strengths; Inkling does not.
1M context: Qwen 3.6 Plus — Qwen 3.6 Plus lists 1M context 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 Qwen 3.6 Plus's $0.325/$1.95 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: Inkling — At Open weight (self-host / free) it undercuts Qwen 3.6 Plus, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs: Inkling — Open weights let you run it on your own hardware; Qwen 3.6 Plus 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 strong gpqa diamond science reasoning: Qwen 3.6 Plus — That is its strongest area.
An enterprise with regional data-residency rules: Inkling or Qwen 3.6 Plus — 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.
Qwen 3.6 Plus: where it fits
Alibaba's open-weight contender — surprising benchmark wins at a budget price. Released April 2, 2026 by Alibaba, it is built for strong GPQA Diamond science reasoning, open-weight and budget-friendly, 1M context, and multilingual coverage.
Its trade-offs: less Western ecosystem tooling, and benchmark coverage still maturing. At $0.325 in / $1.95 out per million tokens, it sits in the budget 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. Qwen 3.6 Plus 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.
Frequently asked questions
Is Inkling or Qwen 3.6 Plus better for coding?
Public SWE-Bench figures are not available for Inkling, 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 Qwen 3.6 Plus leans toward strong gpqa diamond science reasoning, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Inkling or Qwen 3.6 Plus?
Inkling is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen 3.6 Plus is API-metered at $0.325/$1.95 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?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Inkling and Qwen 3.6 Plus together?
Yes — a multi-model platform like LumiChats gives you Inkling, Qwen 3.6 Plus 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 Qwen 3.6 Plus?
Inkling — released July 15, 2026, about 3 months after Qwen 3.6 Plus.
Inkling vs Qwen 3.6 Plus
Thinking Machines Lab · US | Alibaba · 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 Qwen 3.6 Plus for strong gpqa diamond science reasoning or open-weight and budget-friendly. Choose Inkling if you need self-hosting or data privacy; Qwen 3.6 Plus if you want a managed API.
Inkling (Thinking Machines Lab, US) and Qwen 3.6 Plus (Alibaba, 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. Qwen 3.6 Plus is alibaba's open-weight contender — surprising benchmark wins at a budget price. They diverge most on price 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 Qwen 3.6 Plus is API-metered at $0.325/$1.95 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Inkling is the newer model by about 3 months (released July 15, 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
Qwen 3.6 Plus
Provider
Thinking Machines Lab (US)
Alibaba (China)
Released
July 15, 2026
April 2, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$0.325/$1.95 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, audio, code
text, image, code
SWE-Bench Verified
Not published
78.8%
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 — Qwen 3.6 Plus 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 Qwen 3.6 Plus is API-only.
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 it is the newer of the two.
Strong GPQA Diamond science reasoning
Qwen 3.6 Plus
Qwen 3.6 Plus lists strong GPQA Diamond science reasoning among its strengths; Inkling does not.
Open-weight and budget-friendly
Qwen 3.6 Plus
Qwen 3.6 Plus lists open-weight and budget-friendly among its strengths; Inkling does not.
1M context
Qwen 3.6 Plus
Qwen 3.6 Plus lists 1M context 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 Qwen 3.6 Plus's $0.325/$1.95 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
At Open weight (self-host / free) it undercuts Qwen 3.6 Plus, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs
→ Inkling
Open weights let you run it on your own hardware; Qwen 3.6 Plus 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 strong gpqa diamond science reasoning
→ Qwen 3.6 Plus
That is its strongest area.
An enterprise with regional data-residency rules
→ Inkling or Qwen 3.6 Plus
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.
Qwen 3.6 Plus: where it fits
Alibaba's open-weight contender — surprising benchmark wins at a budget price. Released April 2, 2026 by Alibaba, it is built for strong GPQA Diamond science reasoning, open-weight and budget-friendly, 1M context, and multilingual coverage.
Its trade-offs: less Western ecosystem tooling, and benchmark coverage still maturing. At $0.325 in / $1.95 out per million tokens, it sits in the budget 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. Qwen 3.6 Plus 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 Qwen 3.6 Plus 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.
Public SWE-Bench figures are not available for Inkling, 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 Qwen 3.6 Plus leans toward strong gpqa diamond science reasoning, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Inkling or Qwen 3.6 Plus?
Inkling is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen 3.6 Plus is API-metered at $0.325/$1.95 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?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Inkling and Qwen 3.6 Plus together?
Yes — a multi-model platform like LumiChats gives you Inkling, Qwen 3.6 Plus 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 Qwen 3.6 Plus?
Inkling — released July 15, 2026, about 3 months after Qwen 3.6 Plus.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.