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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 profile

Dolphin

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 profile

Should 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

AttributeThinking MachinesDolphin
Model accessOpen weightsOpen weights, uncensored fine-tunes
Flagship modelsInkling, Inkling-SmallDeepSeek V4.1 Flash, Dolphin Mistral 24B
SpeedUnknownUnknown
PricePer 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out$0.01 in, $0.07 out (DeepSeek V4.1 Flash)
CustomizationLoRA SFT and RL via TinkerOwn uncensored fine-tunes
DeploymentTraining API, beta serverless (Inkling only)Decentralized serverless API
Long contextInkling up to 1M (256K serverless); Tinker 32K–256KUnknown

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.

Related comparisons

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.