Thinking Machines vs Dolphin
Thinking Machines trains open models through its Tinker API. Dolphin serves cheap open and uncensored models on a decentralized GPU network.
By The Subconscious Team · Updated
Thinking Machines vs Dolphin: key differences
Thinking Machines' Tinker runs LoRA SFT and RL loops that teams write themselves, and its beta serverless API serves the Apache 2.0 Inkling models at $1.00 in and $4.05 out for Inkling. Checkpoint sampling is scoped to testing. Dolphin's API launched in October 2026 with one model, DeepSeek V4.1 Flash, at $0.01 in and $0.07 out per million tokens, served on idle consumer GPUs across a decentralized network. It publishes no speed, context or SLA, and signup runs through a crypto wallet and Telegram.
Choose Thinking Machines to build a model. Choose Dolphin to call one very cheaply. Dolphin also hosts uncensored models that mainstream clouds will not.
What Thinking Machines and Dolphin 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. In September 2026 it signed a $65M-a-year deal with Crusoe to serve Inkling, GLM 5.2 and 5.3 and its fine-tunes on dedicated B200s. 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 profileDolphin
Dolphin began in 2023 as Eric Hartford's series of open-source, uncensored fine-tunes, trained to drop the refusals and baked-in guidelines of their base models so that the developer sets the rules through the system prompt. Current releases include Dolphin Llama 3.1 8B and Dolphin Mistral 24B Venice Edition, built with Venice, and the project publishes nearly 100 models on Hugging Face. It also runs a chat app, a Telegram bot and a synthetic data service that generates answers to commonly refused prompts.
Example models: DeepSeek V4.1 Flash, Dolphin Mistral 24B
Full Dolphin profileShould you choose Thinking Machines or Dolphin?
Thinking Machines
Choose Thinking Machines for
- Custom SFT and RL on open models
- Training large MoE models without a cluster
- Inkling with 1M context
Dolphin
Choose Dolphin for
- Very low prices on DeepSeek V4.1 Flash
- Cheap background or batch calls
- Models without vendor-imposed refusals
Thinking Machines vs Dolphin at a glance
| Attribute | Thinking Machines | |
|---|---|---|
| Model access | Open weights | Open weights, uncensored fine-tunes |
| Flagship models | Inkling, Inkling-Small | DeepSeek V4.1 Flash, Dolphin Mistral 24B |
| Speed | Unknown | Unknown |
| Price | Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out | $0.01 in, $0.07 out (DeepSeek V4.1 Flash) |
| Customization | LoRA SFT and RL via Tinker | Own uncensored fine-tunes |
| Deployment | Training API, beta serverless (Inkling only) | Decentralized serverless API |
| Long context | Inkling up to 1M (256K serverless); Tinker 32K–256K | Unknown |
Frequently asked questions
What is the difference between Thinking Machines and Dolphin?
Thinking Machines trains open models through its Tinker API. Dolphin serves cheap open and uncensored models on a decentralized GPU network.
When should I choose Thinking Machines over Dolphin?
Custom SFT and RL on open models; Training large MoE models without a cluster; Inkling with 1M context.
When should I choose Dolphin over Thinking Machines?
Very low prices on DeepSeek V4.1 Flash; Cheap background or batch calls; Models without vendor-imposed refusals.
Is Thinking Machines or Dolphin cheaper?
Thinking Machines: Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out. Dolphin: $0.01 in, $0.07 out (DeepSeek V4.1 Flash). The cheaper choice depends on the model and workload.
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Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.