Novita AI
Try Novita AI on us. Starter credit covers your first requests, then one key reaches all 75 Novita AI models, with the privacy posture shown on every route.
Novita AInovita
These privacy labels describe Novita AI, the upstream model provider. Confidential requires all three: verified provider compute, end-to-end encryption and explicit zero data retention. Account and billing metadata are outside the content-retention claim.
| Provider | Novita AI |
|---|---|
| API operator country | US Legal home of the API operator, not an inference-location guarantee. |
| Headquarters | San Francisco, California, United States Company profile. Source; reviewed 2026-09-27. |
| Routing status | Active |
| Provider website | https://novita.ai/ |
| Models | 75 public models |
| Credits routes | 75 |
| Zero data retention | not claimed |
| Verified confidential inference | Not verified |
| Policy note | No provider-ZDR claim is tracked here. Novita's privacy policy says personal information is not used for model training; customer-content processing is governed by customer agreements. Policy source |
Inference locations
Provider API declarations are also available on each model's endpoint API. Direct chat and Responses replies separate advertised availability from serving location in usage.inference_location; the actual serving region remains unknown when not reported.
usage.provider_usage.gateway_region identifies the TrustedRouter gateway, not the provider's inference location. A value such as europe-west4 does not mean the model ran in Europe. usage.inference_location.serving_region remains null when the provider has not reported it. Older gateways may return the gateway-only field as provider_usage.region.
Where the upstream GPUs execute the model, not the provider's headquarters, API ingress, or data storage. ZDR and confidential inference do not by themselves restrict geography.
- Declared countries or regions
- Not verified
No exhaustive inference-country list verified for the shared model API. Novita publishes worldwide GPU rental regions, but that separate product does not establish shared serverless model placement. - Can locations change?
- Shared-route placement and failover geography require provider confirmation.
- Provider region pinning
- GPU-instance or dedicated-deployment region selection is not evidence of a region pin for this shared model API.
- Pinning through TrustedRouter
- Not supported by the current TrustedRouter integration.
- Infrastructure declaration
- Shared inference API; processing countries unconfirmed, no fixed-region commitment.
- Evidence
- Public docs and native catalog reviewed; model-specific geography unconfirmed. Reviewed .
Provider selection alone is not a region pin. TrustedRouter's US provider filter selects company jurisdiction, not GPU location. A regional gateway hostname does not pin a downstream GPU. For a binding residency requirement, confirm the model, region, failover scope and enforced controls before sending sensitive data.
Measured performance
35 samplesContinuously sampled across Novita AI's routed models: p50 TTFT, effective throughput, and success rate. Effective throughput uses provider-reported output tokens over complete request time. Unsupported route and probe-configuration rows are separated from provider downtime. No prompt or output content stored.
| p50 TTFT | 1708 ms |
|---|---|
| Effective throughput | 55 tok/s n=9 |
| Uptime | 94.29% |
| Model | p50 TTFT | Effective throughput | Uptime | Config excluded | Availability samples |
|---|---|---|---|---|---|
| qwen/qwen3.8-27b | 871 ms | — | 100.00% | — | 1 |
| deepseek/deepseek-v4-flash-0731 | 1052 ms | — | 100.00% | — | 2 |
| z-ai/glm-5.3 | 1147 ms | 151 tok/s n=1 | 100.00% | — | 2 |
| zai-org/glm-4.5v | 1227 ms | — | 100.00% | — | 1 |
| inclusionai/ling-3.0-flash | 1253 ms | — | 100.00% | — | 2 |
| zai-org/autoglm-phone-9b-multilingual | 1283 ms | — | 100.00% | — | 1 |
| microsoft/wizardlm-2-8x22b | 1320 ms | — | 100.00% | — | 1 |
| zai-org/glm-5 | 1455 ms | — | 100.00% | — | 2 |
| deepseek/deepseek-ocr-2 | 1496 ms | — | 100.00% | — | 1 |
| mindai/macaron-v1-venti | 1657 ms | — | 100.00% | — | 2 |
| qwen/qwen3-omni-30b-a3b-thinking | 1687 ms | — | 100.00% | — | 1 |
| minimax/minimax-m3 | 1708 ms | 102 tok/s n=2 | 100.00% | — | 1 |
| minimax/minimax-m2.7 | 1760 ms | — | 100.00% | — | 1 |
| deepseek/deepseek-v4.1-flash-1017 | 1808 ms | — | 100.00% | — | 1 |
| moonshotai/kimi-k2.7-code | 1898 ms | 42 tok/s n=1 | 100.00% | — | 1 |
| z-ai/glm-5.3-p | 1960 ms | — | 100.00% | — | 1 |
| moonshotai/kimi-k2.5 | 2136 ms | — | 100.00% | — | 2 |
| minimax/minimax-m3.1-flash | 2228 ms | — | 100.00% | — | 1 |
| z-ai/glm-5.2 | 2237 ms | 62 tok/s n=1 | 100.00% | — | 1 |
| deepseek/deepseek-v4-pro | 2367 ms | — | 100.00% | — | 1 |
| deepseek/deepseek-v4.1-flash-p | 2436 ms | — | 100.00% | — | 1 |
| zai-org/glm-4.7-flash | 2467 ms | — | 100.00% | — | 1 |
| moonshotai/kimi-k3.1-pro-highspeed | 2530 ms | — | 100.00% | — | 1 |
| deepseek/deepseek-v4-flash-0731-p | 2663 ms | — | 100.00% | — | 1 |
| moonshotai/kimi-k2.6 | 3279 ms | 39 tok/s n=2 | 100.00% | — | 1 |
| moonshotai/kimi-k3 | 3516 ms | 55 tok/s n=1 | 100.00% | — | 1 |
| meta-llama/llama-4-scout-17b-16e-instruct | 18434 ms | — | 100.00% | — | 1 |
| baidu/cobuddy | — | — | 0.00% | — | 1 |
| google/gemma-3-12b-it | — | — | 0.00% | — | 1 |
| google/gemma-4-31b-it | — | 32 tok/s n=1 | — | — | 0 |
| moonshotai/kimi-k2-thinking | — | — | — | 1 unsupported_route |
0 |
Novita AI performance history · Full provider & model leaderboard.
Models served by Novita AI.
Each row links to pricing, provider, benchmark, and API pages for the model.
| Model | AI IQ | Context | Input | Cached input | Output |
|---|---|---|---|---|---|
Sao10K/L3-8B-Stheno-v3.2Llama 3 8B Stheno V3.2 |
— | 8,192 | $0.05275/1M | Not published | $0.05275/1M |
baichuan/baichuan-m2-32bBaichuan M2 32B |
— | 131,072 | $0.07385/1M | Not published | $0.07385/1M |
baidu/cobuddyCoBuddy |
— | 131,072 | $0.2954/1M | $0.07385/1M | $1.19215/1M |
baidu/ernie-4.5-21B-a3bERNIE 4.5 21B A3B |
— | 120,000 | $0.07385/1M | Not published | $0.2954/1M |
deepseek/deepseek-ocr-2DeepSeek OCR 2 |
— | 8,192 | $0.03165/1M | Not published | $0.03165/1M |
deepseek/deepseek-r1-0528DeepSeek: R1 0528 |
— | 163,840 | $0.7385/1M | $0.36925/1M | $2.6375/1M |
deepseek/deepseek-r1-turboDeepSeek R1 Turbo |
— | 64,000 | $0.7385/1M | Not published | $2.6375/1M |
deepseek/deepseek-v4-flash-0731DeepSeek: DeepSeek V4 Flash 0731 |
— | 1,048,576 | $0.4642/1M | $0.02954/1M | $1.3926/1M |
deepseek/deepseek-v4-flash-0731-pdeepseek/deepseek-v4-flash-0731-p |
— | 1,048,576 | $0.4642/1M | $0.015614/1M | $1.3926/1M |
deepseek/deepseek-v4-proDeepSeek: DeepSeek V4 Pro 0423 |
IQ 123#34 | 1,048,576 | $1.688/1M | $0.142425/1M | $3.376/1M |
deepseek/deepseek-v4-pro-0813-pdeepseek/deepseek-v4-pro-0813-p |
— | 1,048,576 | $1.3926/1M | $0.04642/1M | $4.1778/1M |
deepseek/deepseek-v4.1-flashDeepSeek: DeepSeek V4.1 Flash |
IQ 126#24 | 1,048,576 | $0.3165/1M | $0.01/1M | $1.266/1M |
deepseek/deepseek-v4.1-flash-1017DeepSeek: DeepSeek V4.1 Flash 1017 |
IQ 128#19 | 1,048,576 | $0.3165/1M | $0.01/1M | $1.266/1M |
deepseek/deepseek-v4.1-flash-dstDeepSeek V4.1 Flash DST |
— | 1,048,576 | $0.3165/1M | $0.01/1M | $1.266/1M |
deepseek/deepseek-v4.1-flash-pDeepSeek V4.1 Flash |
— | 1,048,576 | $0.3165/1M | $0.01/1M | $1.266/1M |
google/gemma-3-12b-itGoogle: Gemma 3 12B |
— | 131,072 | $0.05275/1M | Not published | $0.1055/1M |
google/gemma-3-27b-itGoogle: Gemma 3 27B |
— | 131,072 | $0.125545/1M | Not published | $0.211/1M |
google/gemma-4-26b-a4b-itGoogle: Gemma 4 26B A4B |
IQ 99#131 | 262,144 | $0.13715/1M | Not published | $0.422/1M |
google/gemma-4-31b-itGoogle: Gemma 4 31B |
IQ 101#120 | 262,144 | $0.1477/1M | Not published | $0.422/1M |
inclusionai/ling-3.0-flashinclusionAI: Ling 3.0 Flash |
IQ 104#105 | 262,144 | $0.0633/1M | $0.01266/1M | $0.1899/1M |
meta-llama/llama-3.1-8b-instructMeta: Llama 3.1 8B Instruct |
— | 131,072 | $0.0211/1M | Not published | $0.05275/1M |
meta-llama/llama-3.3-70b-instructMeta: Llama 3.3 70B Instruct |
— | 131,072 | $0.142425/1M | Not published | $0.422/1M |
meta-llama/llama-4-maverick-17b-128e-instruct-fp8Llama 4 Maverick Instruct |
— | 1,048,576 | $0.28485/1M | Not published | $0.89675/1M |
meta-llama/llama-4-scout-17b-16e-instructLlama 4 Scout Instruct |
— | 131,072 | $0.1899/1M | Not published | $0.62245/1M |
microsoft/wizardlm-2-8x22bWizardLM 2 8x22B |
— | 65,535 | $0.6541/1M | Not published | $0.6541/1M |
mindai/macaron-v1-tallMacaron V1 Tall |
— | 262,144 | $0.261113/1M | $0.04642/1M | $1.50865/1M |
mindai/macaron-v1-ventiMacaron V1 Venti |
— | 1,048,576 | $0.870375/1M | $0.174075/1M | $2.611125/1M |
minimax/minimax-m2.5MiniMax: MiniMax M2.5 |
IQ 103#111 | 196,608 | $0.3165/1M | $0.03165/1M | $1.266/1M |
minimax/minimax-m2.7MiniMax: MiniMax M2.7 |
IQ 105#96 | 196,608 | $0.3165/1M | $0.0633/1M | $1.266/1M |
minimax/minimax-m3MiniMax: MiniMax M3 |
IQ 114#64 | 1,048,576 | $0.3165/1M | $0.0633/1M | $1.266/1M |
minimax/minimax-m3.1-flashMiniMax: MiniMax M3.1 Flash |
IQ 120#49 | 1,048,576 | $0.15825/1M | $0.015825/1M | $0.633/1M |
minimaxai/minimax-m1-80kMiniMax M1 |
— | 1,000,000 | $0.58025/1M | Not published | $2.321/1M |
moonshotai/kimi-k2-0905MoonshotAI: Kimi K2 0905 |
— | 262,144 | $0.633/1M | Not published | $2.6375/1M |
moonshotai/kimi-k2-instructKimi K2 Instruct |
IQ 91#159 | 131,072 | $0.60135/1M | Not published | $2.4265/1M |
moonshotai/kimi-k2-thinkingMoonshotAI: Kimi K2 Thinking |
IQ 91#159 | 262,144 | $0.633/1M | $0.15825/1M | $2.6375/1M |
moonshotai/kimi-k2.5MoonshotAI: Kimi K2.5 |
IQ 110#82 | 262,144 | $0.633/1M | $0.1055/1M | $3.165/1M |
moonshotai/kimi-k2.6MoonshotAI: Kimi K2.6 |
IQ 114#62 | 262,144 | $0.844/1M | $0.1688/1M | $3.587/1M |
moonshotai/kimi-k2.7-codeMoonshotAI: Kimi K2.7 Code |
IQ 111#77 | 262,144 | $1.00225/1M | $0.20045/1M | $4.22/1M |
moonshotai/kimi-k3MoonshotAI: Kimi K3 |
IQ 127#22 | 1,048,576 | $3.165/1M | $0.3165/1M | $15.825/1M |
moonshotai/kimi-k3-pKimi K3 |
— | 1,048,576 | $3.165/1M | $0.3165/1M | $15.825/1M |
moonshotai/kimi-k3.1-proMoonshotAI: Kimi K3.1 Pro |
IQ 132#14 | 1,048,576 | $2.11/1M | $0.211/1M | $10.55/1M |
moonshotai/kimi-k3.1-pro-highspeedKimi K3.1 Pro Highspeed |
— | 1,048,576 | $4.22/1M | $0.211/1M | $21.1/1M |
nvidia/nemotron-3-nano-30b-a3bNVIDIA: Nemotron 3 Nano 30B A3B |
— | 262,144 | $0.05275/1M | Not published | $0.211/1M |
openai/gpt-oss-120bOpenAI: gpt-oss-120b |
IQ 98#136 | 131,072 | $0.05275/1M | Not published | $0.26375/1M |
qwen/qwen-mt-plusQwen MT Plus |
— | 16,384 | $0.26375/1M | Not published | $0.79125/1M |
qwen/qwen3-omni-30b-a3b-thinkingQwen3 Omni 30B A3B Thinking |
— | 65,536 | $0.26375/1M | Not published | $1.02335/1M |
qwen/qwen3-vl-30b-a3b-instructQwen: Qwen3 VL 30B A3B Instruct |
— | 131,072 | $0.211/1M | Not published | $0.7385/1M |
qwen/qwen3.5-122b-a10bQwen: Qwen3.5-122B-A10B |
— | 262,144 | $0.422/1M | Not published | $3.376/1M |
qwen/qwen3.5-27bQwen: Qwen3.5-27B |
— | 262,144 | $0.3165/1M | Not published | $2.532/1M |
qwen/qwen3.5-35b-a3bQwen: Qwen3.5-35B-A3B |
— | 262,144 | $0.26375/1M | Not published | $2.11/1M |
qwen/qwen3.5-397b-a17bQwen: Qwen3.5 397B A17B |
— | 262,144 | $0.633/1M | Not published | $3.798/1M |
qwen/qwen3.6-27bQwen: Qwen3.6 27B |
IQ 110#83 | 262,144 | $0.633/1M | Not published | $3.798/1M |
qwen/qwen3.6-35b-a3bQwen: Qwen3.6 35B A3B |
IQ 104#107 | 262,144 | $0.26164/1M | Not published | $1.566675/1M |
qwen/qwen3.7-maxQwen3.7 Max |
IQ 118#55 | 1,000,000 | $1.31875/1M | $0.26375/1M | $3.95625/1M |
qwen/qwen3.8-2.4t-a95bQwen: Qwen3.8 2.4T A95B |
IQ 123#36 | 1,000,000 | $2.11/1M | $0.26375/1M | $6.33/1M |
qwen/qwen3.8-27bQwen: Qwen3.8 27B |
IQ 115#57 | 65,536 | $0.4431/1M | $0.089675/1M | $3.165/1M |
qwen/qwen3.8-flashQwen3.8 Flash |
IQ 115#58 | 1,000,000 | $0.15825/1M | $0.01688/1M | $0.49585/1M |
qwen/qwen3.8-maxQwen3.8 Max |
IQ 123#37 | 1,000,000 | $2.11/1M | $0.26375/1M | $6.33/1M |
stepfun/step-3.7-flashStepFun: Step 3.7 Flash |
IQ 103#113 | 262,144 | $0.211/1M | $0.0422/1M | $1.21325/1M |
stepfun/step-5-previewStepFun: Step 5 Preview |
— | 1,048,576 | $1.055/1M | $0.1055/1M | $2.8485/1M |
tencent/hy3Tencent: Hy3 |
IQ 99#133 | 262,144 | $0.1477/1M | $0.036925/1M | $0.6119/1M |
tencent/hy4-previewTencent: Hy4 preview |
IQ 112#70 | 1,000,000 | $0.87987/1M | $0.04431/1M | $2.638555/1M |
z-ai/glm-5.2Z.ai: GLM 5.2 |
IQ 118#53 | 1,048,576 | $1.477/1M | $0.2743/1M | $4.642/1M |
z-ai/glm-5.3Z.ai: GLM 5.3 |
IQ 124#30 | 1,048,576 | $1.477/1M | $0.2743/1M | $4.642/1M |
z-ai/glm-5.3-flashZ.ai: GLM 5.3 Flash |
IQ 119#50 | 1,048,576 | $0.15825/1M | $0.03165/1M | $0.5275/1M |
z-ai/glm-5.3-pzai-org/glm-5.3-p |
— | 1,048,576 | $1.477/1M | $0.2743/1M | $4.642/1M |
zai-org/autoglm-phone-9b-multilingualAutoGLM Phone 9B Multilingual |
— | 65,536 | $0.036925/1M | Not published | $0.14559/1M |
zai-org/glm-4.5-airGLM 4.5 Air |
— | 131,072 | $0.13715/1M | $0.026375/1M | $0.89675/1M |
zai-org/glm-4.5vGLM 4.5V |
— | 65,536 | $0.633/1M | $0.11605/1M | $1.899/1M |
zai-org/glm-4.6GLM 4.6 |
— | 204,800 | $0.58025/1M | $0.11605/1M | $2.321/1M |
zai-org/glm-4.6vGLM 4.6V |
— | 131,072 | $0.3165/1M | $0.058025/1M | $0.9495/1M |
zai-org/glm-4.7GLM 4.7 |
IQ 104#104 | 204,800 | $0.633/1M | $0.11605/1M | $2.321/1M |
zai-org/glm-4.7-flashGLM 4.7 Flash |
— | 200,000 | $0.07385/1M | $0.01055/1M | $0.422/1M |
zai-org/glm-5GLM 5 |
IQ 106#94 | 202,800 | $1.055/1M | $0.211/1M | $3.376/1M |
zai-org/glm-5.1GLM 5.1 |
IQ 112#67 | 204,800 | $1.4559/1M | $0.2743/1M | $4.642/1M |
Questions
Does Novita AI have zero data retention?
TrustedRouter does not currently mark Novita AI as provider-level zero data retention. Use trustedrouter/zdr or provider.min_privacy=zdr to select a different eligible route, and review the linked policy source for changes.
Is Novita AI end-to-end encrypted?
TrustedRouter does not currently mark Novita AI as end-to-end encrypted at the provider boundary. The TrustedRouter gateway is still attested, but the selected provider normally receives the request in order to run the model. Use trustedrouter/e2e for the stronger route requirement.
Which Novita AI models are available through TrustedRouter?
This page currently lists 75 public Novita AI models, with live pricing, route count, context length, measured performance when available, and links to each model's provider and benchmark pages.