Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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. On a tight budget at scale, Inkling is the value pick.
GLM 5 (Z.ai, China) and Inkling (Thinking Machines Lab, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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 and context window — each quantified below from the models' real specs.
Key differences
Context window: Inkling holds 5× more — 1M (~1,500 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Inkling is the newer model by about 5 months (released July 15, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GLM 5
Inkling
Provider
Z.ai (China)
Thinking Machines Lab (US)
Released
February 12, 2026
July 15, 2026
Context window
200K (~300 pages)
1M (~1,500 pages)
Price (in/out)
$1/$3.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, audio, code
SWE-Bench Verified
77.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows: GLM 5 — GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; Inkling does not.
Complex systems design and backend reasoning: GLM 5 — GLM 5 lists complex systems design and backend reasoning among its strengths; Inkling does not.
Iterative self-correction on autonomous tasks: GLM 5 — GLM 5 lists iterative self-correction on autonomous tasks 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 — 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 carries the larger 1M context.
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 it is the newer of the two.
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; GLM 5 does not.
Lowest cost at scale: Inkling — Its weights are open, so at volume you pay for your own hardware instead of GLM 5's $1/$3.2 per 1M tokens.
Largest single-prompt input: Inkling — Its 1M window is about 5× larger than GLM 5's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Inkling — At Open weight (self-host / free) it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Inkling — Larger 1M window fits more in one prompt.
Anyone whose priority is agentic planning and long-horizon coding workflows: GLM 5 — 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.
An enterprise with regional data-residency rules: Inkling or GLM 5 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget 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
This is less "which is smarter" and more "which ecosystem fits." GLM 5 (China) and Inkling (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Inkling 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.
Frequently asked questions
Is GLM 5 or Inkling 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, GLM 5 leans toward agentic planning and long-horizon coding workflows 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, GLM 5 or Inkling?
Inkling is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Inkling — 1M vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Inkling together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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, GLM 5 or Inkling?
Inkling — released July 15, 2026, about 5 months after GLM 5.
GLM 5 vs Inkling
Z.ai · China | Thinking Machines Lab · US · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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. On a tight budget at scale, Inkling is the value pick.
GLM 5 (Z.ai, China) and Inkling (Thinking Machines Lab, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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 and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Inkling holds 5× more — 1M (~1,500 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Inkling is the newer model by about 5 months (released July 15, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GLM 5
Inkling
Provider
Z.ai (China)
Thinking Machines Lab (US)
Released
February 12, 2026
July 15, 2026
Context window
200K (~300 pages)
1M (~1,500 pages)
Price (in/out)
$1/$3.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, audio, code
SWE-Bench Verified
77.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; Inkling does not.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; Inkling does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks 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
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 carries the larger 1M context.
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 it is the newer of the two.
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; GLM 5 does not.
Lowest cost at scale
Inkling
Its weights are open, so at volume you pay for your own hardware instead of GLM 5's $1/$3.2 per 1M tokens.
Largest single-prompt input
Inkling
Its 1M window is about 5× larger than GLM 5's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
At Open weight (self-host / free) it undercuts GLM 5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Inkling
Larger 1M window fits more in one prompt.
Anyone whose priority is agentic planning and long-horizon coding workflows
→ GLM 5
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.
An enterprise with regional data-residency rules
→ Inkling or GLM 5
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget 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
This is less "which is smarter" and more "which ecosystem fits." GLM 5 (China) and Inkling (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Inkling 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 GLM 5 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.
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, GLM 5 leans toward agentic planning and long-horizon coding workflows 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, GLM 5 or Inkling?
Inkling is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Inkling — 1M vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and Inkling together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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, GLM 5 or Inkling?
Inkling — released July 15, 2026, about 5 months after GLM 5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.