Thinking Machines vs TypeSafe AI

Two labs with ex-OpenAI founders taking different paths. Thinking Machines trains big open generative models. TypeSafe AI builds Jev, a decision model returning typed, calibrated answers in about 100ms.

By The Subconscious Team · Updated September 30, 2026

Thinking Machines vs TypeSafe AI: key differences

TypeSafe AI gives up text generation entirely. A developer defines the answer space with primitives like Choice, Score and a true-or-false type, and Jev returns a typed answer with calibrated probabilities. It scores every option in parallel in one pass, so most calls finish in about 100ms, and TypeSafe says it runs roughly 40 to 200x faster than an LLM on decision-shaped queries. Thinking Machines stays generative. Inkling is a 975B MoE with 1M context and text, image and audio input, served in beta at $1.00 in and $4.05 out, and Tinker lets teams post-train open models with their own SFT or RL loops.

Jev fits inside software as a router, guardrail or smart if-statement, and since outputs always match the schema it cannot return a value outside the options. It is early access, text-only and has no customization listed. Thinking Machines covers what Jev cannot: writing, coding, reasoning over images and audio, and training a model to a team's own reward. Teams could train a classifier on Tinker, but they would need to host it elsewhere, since checkpoint sampling is scoped to testing. For fast routing decisions with a confidence score, Jev is the more direct tool.

What Thinking Machines and TypeSafe AI do

Thinking Machines

Thinking Machines Lab is the San Francisco lab Mira Murati, formerly CTO of OpenAI, founded in February 2025, with OpenAI co-founder John Schulman as chief scientist. It raised about $2 billion at a $12 billion valuation in July 2025 in a round led by Andreessen Horowitz, and in March 2026 signed a multi-year Nvidia deal for one gigawatt of Vera Rubin capacity. Its main developer product is Tinker, an API for post-training open-weight models that launched in October 2025 and is now generally available. Tinker exposes four low-level calls, forward_backward, optim_step, sample and save_state, so teams write their own supervised or reinforcement learning loops while Thinking Machines runs the distributed GPU work. Training uses LoRA adapters rather than full weight updates.

Example models: Inkling, Inkling-Small, Qwen3.5, Kimi K2.6

Full Thinking Machines profile

TypeSafe AI

TypeSafe AI builds decision models instead of text generators. Founder Diogo Almeida co-invented RLHF and InstructGPT at OpenAI and later worked at Google Brain. After two years in stealth the company released its first System One Model, Jev, in early access. The name nods to Kahneman's fast System 1 thinking, and the model is built for machines to call, not people to chat with.

Example models: Jev, jev-1.13

Full TypeSafe AI profile

Should you choose Thinking Machines or TypeSafe AI?

Thinking Machines

Choose Thinking Machines for

Generative tasks across text, image and audio input
Custom RL or SFT post-training
Long-context work up to 1M tokens

TypeSafe AI

Choose TypeSafe AI for

Routing and classification in about 100ms
Guardrails with calibrated confidence
Schema-safe decisions inside agent harnesses

Thinking Machines vs TypeSafe AI at a glance

Attribute	Thinking Machines	TypeSafe AI
Model access	Open weights	Decision models
Flagship models	Inkling, Inkling-Small	Jev, jev-1.13
Speed	Unknown	~100ms per call
Price	Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out	A fraction of an LLM call
Customization	LoRA SFT and RL via Tinker	Unknown
Deployment	Training API, beta serverless (Inkling only)	Early-access API
Long context	Inkling up to 1M; Tinker 32K–256K	Unknown

Frequently asked questions

What is the difference between Thinking Machines and TypeSafe AI?

When should I choose Thinking Machines over TypeSafe AI?

Generative tasks across text, image and audio input; Custom RL or SFT post-training; Long-context work up to 1M tokens.

When should I choose TypeSafe AI over Thinking Machines?

Routing and classification in about 100ms; Guardrails with calibrated confidence; Schema-safe decisions inside agent harnesses.

Is Thinking Machines or TypeSafe AI cheaper?

Thinking Machines: Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out. TypeSafe AI: A fraction of an LLM call. The cheaper choice depends on the model and workload.

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