TypeSafe AI
Decision models that return typed answers with calibrated confidence in milliseconds.
- Founded
- 2024
- Example models
- Jev, jev-1.13
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.
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, 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-1.13 | 70–500ms per call | $0.042 per 1M input tokens; output free | Unknown | Early-access API, also via LiteLLM | 32K | |
 Subconscious | Open weights | GLM 5.3, DeepSeek V4.1 Flash | 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 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 3.8 Flash, Claude, Gemma | Flash tier built for low latency | Gemini 3.8 Flash $0.75 in, $3.75 out until 2027, then $1.50/$7.50 | Custom training on GPUs or TPUs | Managed on Google Cloud | 1M on Gemini 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 | 0.99s TTFT on DeepSeek V4 Pro | Parity with Fireworks and Baseten | LoRA and full SFT; RL in beta | Serverless, dedicated, GPU clusters | 512K on DeepSeek V4 Pro 0813 | 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, RFT; Training API | Serverless, dedicated GPUs | Full 1M on DeepSeek V4.1 Flash, GLM 5.3 | Compare |
 Baseten | Open weights, about a dozen 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 | 500K (4.7), 1M (4.20, 4.3) | Compare |
 Mistral AI | Open weights, plus closed Codestral | Mistral Large 4, Medium 3.5, Small 4, Large 3 | 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 (Large 4 preview); 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, Kimi K2.8 Preview | ~33 tok/s on Kimi K3 | $3 in, $15 out (Kimi 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 Glimmer | Muse Spark 1.3, Muse Glimmer | ~145–233 tok/s on Muse Spark 1.3 | $1.25 in, $4.25 out; Contributor tier cheaper | Open Muse Glimmer weights to fine-tune | Meta Model API (preview) | 1M | Compare |
 Cohere | Closed, plus open Command A+ | Command A+, Command A, Embed 5, Rerank 4 | 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, MiniMax M2.7, GPT-OSS 120B | ~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, Kimi K2.6, 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 | $0.06–$12 in, $0.28–$60 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 | Specialist models | relace-apply-3, agentic search | ~10,000 tok/s apply | 3x+ cheaper than full rewrites | Unknown | Hosted API or self-hosted | 128K max | Compare |
 StepFun | Open (Apache 2.0) 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 | ~176 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 | DeepSeek V4.1 Flash, GLM 5.3 Flash | ~193 tok/s on DeepSeek V4.1 Flash | $0.10 in, $0.40 out (GLM 5.3 Flash) | Unknown | Via 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 2.5, Mercury 2 | 1,100+ tok/s (vendor); ~640 measured | $0.20 in, $0.75 out (Mercury 2.5) | Fine-tuning on request | Serverless API, Bedrock, Azure, private | 260K | 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 |
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.
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: 32K.
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, 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. Pricing and model lineups change often; figures are a snapshot.