TypeSafe AI
Decision models that return typed answers with calibrated confidence in milliseconds.
- Founded
- 2024
- Example models
- Jev, jev-1.14
By The Subconscious Team · Updated
What is 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; his co-founders are Erik Gafni and Sasha Sheng. After two years in stealth the company launched on September 15, 2026 with a $40M seed round led by DCVC and 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. Weeks after launch, in October 2026, TypeSafe raised an $870M Series A led by Andreessen Horowitz at a $7.5B valuation. Jev 1.14 followed on October 14 at the same price, adding experimental image input and longer context behind opt-in flags.
Jev gives up free-form strings. A developer defines the answer space up front with primitives like Choice, Score and a true-or-false type, and Jev returns a typed answer plus calibrated probabilities and a confidence score. It evaluates every option in parallel in one pass rather than token by token, so calls finish in 70 to 500ms end to end and cost $0.042 per million input tokens, with output free. A new training method, Reinforcement Learning for Calibrated Decisions, targets probabilities that track real accuracy. Because outputs always match the schema, Jev cannot produce a type error or hallucinate a value outside the options.
TypeSafe AI pros, cons and use cases
Upsides
- Roughly 40 to 200x faster than an LLM on decision-shaped queries, at a fraction of the cost.
- Calibrated confidence lets software act automatically when sure and escalate when not.
Core use cases
- Smart if-statements inside workflows, like routing tickets, scoring leads or classifying intent.
- Guardrails and routers inside agent harnesses, such as detecting jailbreaks, grading tool calls or picking which LLM gets a prompt.
Downsides
- No text or code generation, so it complements an LLM rather than replacing one.
- Early access, text-only input outside an experimental image mode, and a new programming model teams have to learn.
TypeSafe AI alternatives compared
Pick any row for the full head-to-head.
| Provider | Model access | Flagship models | Speed | Price | Customization | Deployment | Long context | Compare |
|---|---|---|---|---|---|---|---|---|
 TypeSafe AI | Decision models | Jev, jev-1.14 | 70–500ms per call | $0.042 per 1M input tokens; output free | Unknown | Early-access API, plus Vercel AI Gateway, OpenRouter and LiteLLM | 64K per request, 32K per question | |
 Subconscious | Open weights | GLM 5.3 Marathon, DeepSeek V4.1 1017 Marathon, Nemotron 3.5 Super VL | 2x faster task completion | 50–80% lower cost; billed on processed tokens | Marathon post-trained variants | Managed API, dedicated, on-prem | 5M+ effective context | Compare |
 OpenAI | Closed, plus open gpt-oss | GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, GPT-6 Luna | Fast mode; Ultrafast on GPT-6 Astra | $0.10–$10 in, $0.50–$50 out per 1M | N/A | API, Azure OpenAI, Bedrock | 1.05M; 2x input past 272K | Compare |
 Anthropic | Closed | Claude Fable 5.1, Opus 5.5, Sonnet 5.5, Haiku 5.5 | Fable is the slowest tier | $0.10–$10 in, $0.50–$50 out per 1M | N/A | API, Bedrock, Vertex AI, Microsoft Foundry | 1M; Haiku 5.5 costs more past 100K | Compare |
 Google Vertex AI | Closed and open, 200+ models | Gemini 4 Argon, Gemini 3.8 Flash, Claude, Gemma | Flash tier built for low latency | Argon $2 in, $10 out until Dec 1, then $4/$20; 3.8 Flash $0.75/$3.75 until 2027 | Custom training on GPUs or TPUs | Managed on Google Cloud | 1M on Gemini 4 Argon and 3.8 Flash | Compare |
 Amazon Bedrock | Closed and open, 100+ models | Claude, GPT-6 Astra, Nova, DeepSeek | Latency-optimized option on some models | ~20–35% above direct; Claude and GPT at parity | Fine-tuning, Custom Model Import | Managed on AWS, AgentCore | Varies by model | Compare |
 Together AI | Open weights | Kimi K3, GLM 5.3, DeepSeek V4.1 Flash, Qwen 3.8 Flash | 0.99s TTFT on DeepSeek V4 Pro | Parity with Fireworks and Baseten | LoRA and full SFT; RL via Post-Training | Serverless, dedicated, GPU clusters | 1M on DeepSeek V4 Pro 0813, Kimi K3, GLM 5.3 | Compare |
 Fireworks AI | Open weights | DeepSeek V4.1 Flash, GLM 5.3, Kimi K3 | 167–174 tok/s on DeepSeek V4 Pro | Fine-tunes served at base price | SFT, DPO; RL via Training API | Serverless, dedicated GPUs | Full 1M on DeepSeek V4.1 Flash, GLM 5.3 | Compare |
 Baseten | Open weights, 15+ curated | GLM 5.3, DeepSeek V4.1 Flash, Kimi K3, gpt-oss 120B | 0.49s TTFT, lowest measured | H100 about $6.50/hr dedicated | Deploy any model with Truss | Model APIs, dedicated, self-host | Varies by model | Compare |
 Groq | Open weights | GPT-OSS 120B, Qwen 3.8 27B | 500–1,000 tok/s | Near the floor on small models | No fine-tuned model hosting | GroqCloud API | Around 131K max | Compare |
 Cerebras | Open weights | GPT-OSS 120B, Qwen 3.8 27B | ~3,000 tok/s on GPT-OSS 120B | $0.35 in, $0.75 out (GPT-OSS 120B) | Unknown | Shared API, dedicated, partners | 131K on GPT-OSS 120B, 128K on Qwen 3.8 27B | Compare |
 DeepInfra | Open weights | DeepSeek V4 Flash, Llama 3.1 8B | Unknown | From $0.02 per 1M | No managed fine-tuning | Shared API, no contracts | 1M on DeepSeek V4 Pro | Compare |
 Hugging Face Inference Providers | Open weights | GLM 5.3, Kimi K3, DeepSeek V4.1 Flash | Routes to fastest provider by default | Provider rates, no markup | N/A | Serverless router; dedicated Endpoints | Up to 1M, provider-dependent | Compare |
 Modal | Bring your own weights | None hosted | ~1s container boot | Per second; H100 $3.95/hr list | Run any training code | Serverless GPU containers | Depends on the model you deploy | Compare |
 Cloudflare Workers AI | Open weights | DeepSeek V4 Pro, GLM 5.3, Kimi K3, Kimi K2.7 Code, gpt-oss 120B | Unknown | $0.011 per 1K Neurons; 10K free daily | BYO LoRA on small models (beta) | Serverless on Cloudflare network | 1M on DeepSeek V4 and Kimi K3; 262K on Kimi K2.6 and K2.7 Code | Compare |
 xAI | Closed | Grok 4.7, Grok 4.20, grok-build | ~54 tok/s on Grok 4.6 | $2 in, $6 out (Grok 4.7); 2x past 200K | Unknown | First-party API, Amazon Bedrock | 500K (4.7), 1M (4.20, 4.3) | Compare |
 Mistral AI | Open weights, plus closed Codestral | Mistral Large 4, Small 4, Codestral | Unknown | $0.15–$1.50 in, $0.60–$7.50 out per 1M | Forge (enterprise); fine-tuning API deprecated | API, Azure, Bedrock, Vertex, self-host | 1M on Large 4; 256K on others | Compare |
 DeepSeek | Open weights (MIT) | DeepSeek V4.1 Flash | ~209 tok/s on V4.1 Flash | Off-peak hours at half price | Open weights to fine-tune | First-party API, Hugging Face weights | 1M, 384K max output | Compare |
 Moonshot AI | Open weights, custom license | Kimi K3.1 Pro, Kimi K3 | ~33 tok/s on Kimi K3 | $2 in, $10 out (K3.1 Pro); $3 in, $15 out (K3) | Open weights to fine-tune | API, Kimi Code, OpenRouter | 1M | Compare |
 Z.ai | Open weights (GLM-5.3 License; MIT on Flash and older) | GLM-5.3, GLM-5.3-Flash | ~80 tok/s on GLM-5.3 | $1.40 in, $4.40 out (GLM-5.3); free Flash tier | Open weights; GLM-5.3 License adds review for large hosts | API, GLM Coding Plan | 1M (GLM-5.3) | Compare |
 Alibaba Cloud | Open weights incl. Max (custom license) | Qwen 3.8-Max, Qwen 3.7-Max | ~40 tok/s on Qwen 3.8-Max | $2 in, $6 out international | No fine-tuning on hosted Max | Model Studio on Alibaba Cloud | 1M (Qwen 3.8-Max) | Compare |
 Meta | Closed API; open Muse Spark Open and Glimmer | Muse Wonder, Muse Spark 1.3, Muse Spark Open | ~145–233 tok/s on Muse Spark 1.3 | $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper | Open Muse weights to fine-tune | Meta Model API, Oracle Cloud | 1M | Compare |
 Cohere | Closed, plus open Command A+ | Command A+, Command A, Embed 5, Rerank 5 | 375 tok/s on Command A+ W4A4, per Cohere | $0.0375–$2.50 in, $0.15–$10 out per 1M | Enterprise fine-tuning, incl. private | API, Bedrock, Azure, OCI, VPC, on-prem | 256K on Command A; 128K on A+ | Compare |
 SambaNova | Open weights | MiniMax M3, GPT-OSS 120B, DeepSeek V3.1 | ~800 tok/s on MiniMax M2.7 (SN50, SemiAnalysis) | $0.22 in, $0.59 out (GPT-OSS 120B) | Unknown | SambaCloud, racks for neoclouds | Up to 1M (MiniMax M3, preview) | Compare |
 Nebius | Open weights, 60+ models | DeepSeek V4 Pro, Kimi K3, MiniMax M3, GLM | Among top hosts on throughput | From $0.06 per 1M input | Serve uploaded fine-tunes | Token Factory, dedicated, raw GPUs | Varies by model | Compare |
 Crusoe | Open weights | DeepSeek V4, GLM 5.3, Qwen3.8 27B, Nemotron 3 | Up to 9.9x faster TTFT vs vLLM (vendor claim) | $0.05–$1.74 in, $0.20–$4.40 out per 1M | Serverless LoRA fine-tuning | Serverless, self-serve and tailored dedicated, raw GPUs | Varies by model; cluster-wide KV cache | Compare |
 fal | Hosted media models | FLUX, Kling, Seedream | Cold starts on less popular endpoints | Per image, per video second, GPU time | LoRA training endpoints | Hosted API, serverless GPUs | Not applicable | Compare |
 Novita AI | Open weights | DeepSeek V4 Pro, Gemma 4 | ~36 tok/s on DeepSeek V4 Pro | From $0.02 per 1M; batch 50% off | Hot-swappable LoRA adapters | Serverless, GPU cloud, dedicated | Full 1M on DeepSeek V4 Pro | Compare |
 Venice | Open weights, plus proxied closed models | GLM 5.3, Kimi K3, DeepSeek V4 Pro | Unknown | $0.05–$30 in, $0.13–$180 out per 1M; DIEM staking | Unknown | Serverless API, consumer app | 1M on most current models | Compare |
 Parasail | Any Hugging Face model | Kimi K3, DeepSeek V4.1 Flash, GLM-5.3, MiniMax M3 | 600ms p99 real-time budget | Per-parameter rates; batch 50% off | Private Hugging Face repos | Serverless, elastic, dedicated, batch | Varies by model | Compare |
 Inference.net | Open, closed and custom | Schematron, ClipTagger, customer fine-tunes | Batch windows of 24h to 7 days | Free pay-as-you-go; Growth $250/mo; B200 $9.98/hr | Distill traces into custom models | Batch API, gateway, dedicated GPUs | Varies by model | Compare |
 GMI Cloud | Open and third-party models | DeepSeek V4 Flash, GLM-5.2, Google Veo | Near bare-metal performance | H100 from $2.60/GPU-hr | Unknown | Shared, autoscaling, reserved GPUs | Varies by model | Compare |
| Thinking Machines | Open weights | Inkling, Inkling-Small | Unknown | Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out | LoRA SFT and RL via Tinker | Training API, beta serverless (Inkling only) | Inkling up to 1M (256K serverless); Tinker 32K–256K | Compare |
 Sail Research | Open weights | Kimi K2.6, GLM-5.3, GPT-OSS 120B | Low-latency default; slower windows cheaper | 30–85% below other hosts by completion window | Customer LoRA fine-tunes | API plus Sailboxes | Varies by model | Compare |
 Morph | Specialist models | morph-v3-fast, morph-v3-large | 10,500+ tok/s Fast Apply | $0.80 in, $1.20 out (morph-v3-fast) | Fine-tuning offered | OpenAI compatible API; self-hosted on Enterprise | Unknown | Compare |
 Relace | Open weights, plus specialist coding models | DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3 | ~10,000 tok/s apply | $0.07 in, $0.50 out (DeepSeek V4.1 Flash) | Unknown | Hosted API, OpenRouter or self-hosted | 128K on apply; varies by open model | Compare |
 StepFun | Open weights (Apache 2.0 or StepFun license) and API models | Step 5 Preview, Step 3.7 Flash | ~128 tok/s on Step 3.7 Flash | $0.20 in, $1.15 out (Step 3.7 Flash) | Open weights to fine-tune | First-party API, OpenRouter | Up to 1M (Step 5 Preview); 256K on Step 3.7 Flash | Compare |
 Runware | Hosted media models | Seedance 2.5, Qwen-Image-3.0 | Unknown | Images from fractions of a cent | Fine-tuned diffusion checkpoints | Unified API, serverless GPUs, bare metal | Not applicable | Compare |
 StreamLake | Proprietary coding models, plus hosted open models | KAT-Coder-Pro V2.5, KAT-Coder-Air V2.5 | Unknown | $0.74 in, $2.96 out (Pro V2.5), or KwaiKAT Coding Plan | Unknown | MaaS API, bare metal | 256K | Compare |
 Wafer | Open weights | GLM-5.3, DeepSeek V4.1 Flash | ~168 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) | Per token, prepaid credits | Agent-tuned dedicated deployments | Serverless, dedicated | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) | Compare |
 RunInfra | Open weights | Nemotron 3.5 Lightning 30B, Qwen 3.8 27B | ~541 tok/s on Nemotron 3.5 Lightning 30B (vendor); cold starts under 2s | From $0.05 in, $0.15 out; coding plans from $10 a month | Uploads up to 50 GB; auto-quantization | Model APIs, agent-built endpoints | Varies by model | Compare |
 Particle.AI | Open weights plus its own Particle-1 tune | Particle-1, DeepSeek V4.1 Flash, GLM 5.3 Flash | ~110 tok/s on Particle-1 (Vercel AI Gateway) | $0.15 in, $0.50 out (GLM 5.3 Flash); $0.25 in, $1 out (Particle-1) | Unknown | Serverless API, Vercel AI Gateway | 1M | Compare |
 Luminal | Small hosted catalog plus bring your own weights | DeepSeek V4.1 Flash, Ideogram 4.5 | 36K tok/s on GPT-OSS 120B, 8xH100 (vendor) | $0.1425 in, $0.57 out (DeepSeek V4.1 Flash) | Compiles any PyTorch or HF model | Serverless (early access), on-prem license | Unknown | Compare |
 Infron | Closed and open, 400+ models | DeepSeek, Qwen, Claude, Gemini, GPT | Unknown | Provider rates; 3–5% top-up fee | Custom deployments | Gateway API, dedicated, BYOK | Varies by model | Compare |
 Inception | Closed weights (Mercury dLLMs) | Mercury 3, Mercury Voice | ~1,070 tok/s on Mercury 3 (vendor) | $0.20 in, $0.70 out (Mercury 3); 50% off at launch | Fine-tuning on request | Serverless API, Bedrock, Azure, private | 1M | Compare |
 Dolphin | Open weights, uncensored fine-tunes | DeepSeek V4.1 Flash, Dolphin Mistral 24B | Unknown | $0.01 in, $0.07 out (DeepSeek V4.1 Flash) | Own uncensored fine-tunes | Decentralized serverless API | Unknown | Compare |
 OpenScale | Open weights | DeepSeek V4.1 Flash, GLM 5.3 Flash | 200+ tok/s; fast tier ~2x | $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash) | N/A | Serverless API | 1M context | Compare |
 General Compute | Open weights | MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B | 1,000+ tok/s on GPT-OSS 120B (vendor) | $0.21–$0.28 in, $0.38–$1.20 out per 1M | Custom LoRA and fine-tuned checkpoints | Serverless API, dedicated racks, bare metal | 192K (MiniMax M2.7) | Compare |
 Boundless | Open weights, plus closed GPT and Claude | GLM-5.3, DeepSeek V4.1 Flash, Kimi K3 | 342 tok/s on DeepSeek V4.1 Flash 1017 (Vercel) | $0.04–$2.30 in, $0.21–$11.40 out per 1M (open) | Serving-stack tuning, no fine-tuning | Serverless API, reserved tailored capacity | 1M context on most models | Compare |
Frequently asked questions
What is 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; his co-founders are Erik Gafni and Sasha Sheng. After two years in stealth the company launched on September 15, 2026 with a $40M seed round led by DCVC and 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. Weeks after launch, in October 2026, TypeSafe raised an $870M Series A led by Andreessen Horowitz at a $7.5B valuation. Jev 1.14 followed on October 14 at the same price, adding experimental image input and longer context behind opt-in flags.
What is TypeSafe AI best for?
Smart if-statements inside workflows, like routing tickets, scoring leads or classifying intent; Guardrails and routers inside agent harnesses, such as detecting jailbreaks, grading tool calls or picking which LLM gets a prompt.
How much does TypeSafe AI cost?
TypeSafe AI pricing at a glance: $0.042 per 1M input tokens; output free. Rates change often, so check TypeSafe AI's pricing page before committing.
How much context does TypeSafe AI support?
TypeSafe AI's long-context support: 64K per request, 32K per question.
What are the downsides of TypeSafe AI?
No text or code generation, so it complements an LLM rather than replacing one; Early access, text-only input outside an experimental image mode, and a new programming model teams have to learn.
What are the best alternatives to TypeSafe AI?
Common alternatives include Subconscious, OpenAI, Anthropic, Google Vertex AI, Amazon Bedrock. Each has a head-to-head comparison with TypeSafe AI on this site.
Sources: TypeSafe AI, Introducing System One Models and Jev, TypeSafe team, TypeSafe AI launch, InfoQ on Jev, Jev on LiteLLM, TypeSafe models, Jev 1.14, TypeSafe Series A. Pricing and model lineups change often; figures are a snapshot.