数据状态
同步自 OpenRouter Providers · 117 家托管商 · source=openrouter · 2026-11-03T16:38:15.628Z
DeepInfra
总部 美国
67 模型110 端点日可用 97.0%
托管模型67
端点110
日可用率均97.0%
最低输入 $/M$0.020
最大上下文1.0M
托管端点 · 110
来自 OpenRouter 各模型 Endpoints API：同一模型可能有多量化 / 多区域端点。
| 模型 | Tag | 量化 | 上下文 | 输入 | 输出 | 5m | 30m | 1d | 延迟 | 吞吐 | 缓存 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| llama-3.1-8b-instruct meta-llama/llama-3.1-8b-instruct | deepinfra/fp8 | fp8 | 131k | $0.020 | $0.040 | 100.00% | 100.00% | 100.00% | — | — | — |
| mistral-nemo mistralai/mistral-nemo | deepinfra/fp8 | fp8 | 131k | $0.029 | $0.030 | 100.00% | 100.00% | 100.00% | — | — | — |
| gpt-oss-120b openai/gpt-oss-120b | deepinfra/bf16 | bf16 | 131k | $0.037 | $0.170 | 100.00% | 100.00% | 100.00% | — | — | — |
| gpt-oss-120b openai/gpt-oss-120b | deepinfra/bf16 | bf16 | 131k | $0.037 | $0.170 | 100.00% | 100.00% | 100.00% | — | — | — |
| l3-lunaris-8b sao10k/l3-lunaris-8b | deepinfra/turbo | fp8 | 8k | $0.040 | $0.050 | 100.00% | 100.00% | 100.00% | — | — | — |
| gemma-3-4b-it google/gemma-3-4b-it | deepinfra/bf16 | bf16 | 131k | $0.050 | $0.100 | 100.00% | 100.00% | 100.00% | — | — | — |
| mistral-small-24b-instruct-2501 mistralai/mistral-small-24b-instruct-2501 | deepinfra/fp8 | fp8 | 33k | $0.050 | $0.080 | 100.00% | 100.00% | 100.00% | — | — | — |
| deepseek-v4-flash-0731 deepseek/deepseek-v4-flash-0731 | deepinfra/fp8 | fp8 | 1.0M | $0.060 | $0.180 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| gemma-4-26b-a4b-it google/gemma-4-26b-a4b-it | deepinfra/fp8 | fp8 | 262k | $0.070 | $0.340 | 100.00% | 100.00% | 100.00% | — | — | — |
| gemma-4-26b-a4b-it google/gemma-4-26b-a4b-it | deepinfra/fp8 | fp8 | 262k | $0.070 | $0.340 | 100.00% | 100.00% | 100.00% | — | — | — |
| phi-4 microsoft/phi-4 | deepinfra/bf16 | bf16 | 16k | $0.070 | $0.140 | 100.00% | 100.00% | 100.00% | — | — | — |
| qwen3-next-80b-a3b-instruct qwen/qwen3-next-80b-a3b-instruct | deepinfra/fp8/flex | fp8 | 262k | $0.072 | $0.880 | — | 100.00% | 100.00% | — | — | — |
| nemotron-3-super-120b-a12b nvidia/nemotron-3-super-120b-a12b | deepinfra/bf16 | bf16 | 262k | $0.085 | $0.400 | 100.00% | 100.00% | 100.00% | — | — | — |
| deepseek-v4-flash deepseek/deepseek-v4-flash | deepinfra/fp8 | fp8 | 1.0M | $0.090 | $0.180 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| deepseek-v4.1-flash-1017 deepseek/deepseek-v4.1-flash-1017 | deepinfra/fp8 | fp8 | 1.0M | $0.098 | $0.294 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| qwen3.5-9b qwen/qwen3.5-9b | deepinfra/bf16 | bf16 | 262k | $0.100 | $0.150 | 100.00% | 100.00% | 100.00% | — | — | — |
| llama-4-scout meta-llama/llama-4-scout | deepinfra/fp8 | fp8 | 328k | $0.100 | $0.300 | — | — | 100.00% | — | — | — |
| qwen3-30b-a3b qwen/qwen3-30b-a3b | deepinfra/fp8 | fp8 | 41k | $0.120 | $0.500 | 100.00% | 100.00% | 100.00% | — | — | — |
| qwen3-next-80b-a3b-instruct qwen/qwen3-next-80b-a3b-instruct | deepinfra/fp8/priority | fp8 | 262k | $0.135 | $1.65 | — | 100.00% | 100.00% | — | — | — |
| deepseek-v4.1-flash deepseek/deepseek-v4.1-flash | deepinfra/fp8 | fp8 | 1.0M | $0.140 | $0.420 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| deepseek-v4.1-flash deepseek/deepseek-v4.1-flash | deepinfra/fp8 | fp8 | 1.0M | $0.140 | $0.420 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| deepseek-v4.1-flash-1017 deepseek/deepseek-v4.1-flash-1017 | deepinfra/fp8/priority | fp8 | 1.0M | $0.147 | $0.441 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| glm-5.3-flash z-ai/glm-5.3-flash | deepinfra/fp4 | fp4 | 1.0M | $0.150 | $0.500 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| glm-5.3-flash z-ai/glm-5.3-flash | deepinfra/fp4 | fp4 | 1.0M | $0.150 | $0.500 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| qwen3.8-27b qwen/qwen3.8-27b | deepinfra/bf16 | bf16 | 262k | $0.150 | $1.88 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/fp8/flex | fp8 | 262k | $0.160 | $0.320 | — | 100.00% | 100.00% | — | — | — |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/fp8/flex | fp8 | 262k | $0.160 | $0.320 | — | 100.00% | 100.00% | — | — | — |
| nemotron-3.5-content-safety nvidia/nemotron-3.5-content-safety | deepinfra/bf16 | bf16 | 131k | $0.200 | $0.200 | — | 100.00% | 100.00% | — | — | — |
| nemotron-3.5-content-safety nvidia/nemotron-3.5-content-safety | deepinfra/bf16 | bf16 | 131k | $0.200 | $0.200 | — | 100.00% | 100.00% | — | — | — |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/fp8 | fp8 | 262k | $0.200 | $0.400 | 100.00% | 100.00% | 100.00% | — | — | — |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/fp8 | fp8 | 262k | $0.200 | $0.400 | 100.00% | 100.00% | 100.00% | — | — | — |
| deepseek-v4.1-flash deepseek/deepseek-v4.1-flash | deepinfra/fp8/priority | fp8 | 1.0M | $0.210 | $0.630 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| deepseek-v4.1-flash deepseek/deepseek-v4.1-flash | deepinfra/fp8/priority | fp8 | 1.0M | $0.210 | $0.630 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| minimax-m3 minimax/minimax-m3 | deepinfra/fp8/flex | fp8 | 524k | $0.224 | $0.880 | — | — | 100.00% | — | — | 是 |
| qwen3.5-27b qwen/qwen3.5-27b | deepinfra/fp8 | fp8 | 262k | $0.260 | $2.60 | 100.00% | 100.00% | 100.00% | — | — | — |
| minimax-m3 minimax/minimax-m3 | deepinfra/fp8 | fp8 | 524k | $0.280 | $1.10 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| muse-glimmer-30b meta/muse-glimmer-30b | deepinfra/bf16 | bf16 | 131k | $0.300 | $1.20 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| qwen3.6-27b qwen/qwen3.6-27b | deepinfra/fp8 | fp8 | 262k | $0.320 | $3.20 | 100.00% | 100.00% | 100.00% | — | — | — |
| glm-4.7 z-ai/glm-4.7 | deepinfra/fp4/flex | fp4 | 203k | $0.320 | $1.40 | — | — | 100.00% | — | — | 是 |
| mimo-v2.6-pro xiaomi/mimo-v2.6-pro | deepinfra/fp8 | fp8 | 1.0M | $0.387 | $0.783 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| nemotron-3-ultra-550b-a55b nvidia/nemotron-3-ultra-550b-a55b | deepinfra/fp4/flex | fp4 | 262k | $0.400 | $1.76 | — | — | 100.00% | — | — | 是 |
| nemotron-3-ultra-550b-a55b nvidia/nemotron-3-ultra-550b-a55b | deepinfra/fp4/flex | fp4 | 262k | $0.400 | $1.76 | — | — | 100.00% | — | — | 是 |
| glm-4.7 z-ai/glm-4.7 | deepinfra/fp4 | fp4 | 203k | $0.400 | $1.75 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| inkling-small thinkingmachines/inkling-small | deepinfra/fp8 | fp8 | 524k | $0.450 | $1.20 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| inkling-small thinkingmachines/inkling-small | deepinfra/fp8 | fp8 | 524k | $0.450 | $1.20 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| glm-5.2 z-ai/glm-5.2 | deepinfra/fp4/flex | fp4 | 1.0M | $0.450 | $1.44 | — | — | 100.00% | — | — | 是 |
| nemotron-3-ultra-550b-a55b nvidia/nemotron-3-ultra-550b-a55b | deepinfra/fp4 | fp4 | 262k | $0.500 | $2.20 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| nemotron-3-ultra-550b-a55b nvidia/nemotron-3-ultra-550b-a55b | deepinfra/fp4 | fp4 | 262k | $0.500 | $2.20 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| deepseek-r1-0528 deepseek/deepseek-r1-0528 | deepinfra/fp4 | fp4 | 164k | $0.500 | $2.15 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| kimi-k2.6 moonshotai/kimi-k2.6 | deepinfra/fp4 | fp4 | 262k | $0.750 | $3.50 | — | — | 100.00% | — | — | 是 |
| hy4-preview tencent/hy4-preview | deepinfra/fp4 | fp4 | 1.0M | $0.834 | $2.50 | 100.00% | 100.00% | 100.00% | — | — | 是 |
| l3.1-euryale-70b sao10k/l3.1-euryale-70b | deepinfra/fp8 | fp8 | 131k | $0.850 | $0.850 | — | 100.00% | 100.00% | — | — | — |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/turbo | fp4 | 262k | $0.135 | $0.510 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/turbo | fp4 | 262k | $0.135 | $0.510 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| deepseek-chat-v3.1 deepseek/deepseek-chat-v3.1 | deepinfra/fp4 | fp4 | 164k | $0.250 | $0.950 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| deepseek-v4-pro-0813 deepseek/deepseek-v4-pro-0813 | deepinfra/fp8 | fp8 | 1.0M | $1.30 | $2.60 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| deepseek-v4-pro deepseek/deepseek-v4-pro | deepinfra/fp8 | fp8 | 1.0M | $1.30 | $2.60 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| kimi-k3 moonshotai/kimi-k3 | deepinfra/mxfp4 | mxfp4 | 1.0M | $2.85 | $14.3 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| kimi-k3 moonshotai/kimi-k3 | deepinfra/mxfp4 | mxfp4 | 1.0M | $2.85 | $14.3 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| ling-3.0-flash-vl inclusionai/ling-3.0-flash-vl | deepinfra/fp16 | fp16 | 131k | $0.060 | $0.180 | 100.00% | 100.00% | 99.93% | — | — | 是 |
| qwen3.5-397b-a17b qwen/qwen3.5-397b-a17b | deepinfra/fp8 | fp8 | 262k | $0.450 | $3.00 | 100.00% | 100.00% | 99.90% | — | — | 是 |
| glm-4.6 z-ai/glm-4.6 | deepinfra/fp4 | fp4 | 203k | $0.500 | $2.00 | 100.00% | 100.00% | 99.8% | — | — | 是 |
| deepseek-v4-flash-vision-exp deepseek/deepseek-v4-flash-vision-exp | deepinfra/fp8 | fp8 | 1.0M | $0.216 | $0.647 | 100.00% | 100.00% | 99.8% | — | — | 是 |
| llama-3.3-70b-instruct meta-llama/llama-3.3-70b-instruct | deepinfra/turbo | fp8 | 131k | $0.100 | $0.320 | 100.00% | 100.00% | 99.8% | — | — | — |
| ling-3.1-flash inclusionai/ling-3.1-flash | deepinfra/fp4 | fp4 | 262k | $0.075 | $0.225 | 100.00% | 100.00% | 99.8% | — | — | 是 |
| qwen3-32b qwen/qwen3-32b | deepinfra/fp8 | fp8 | 41k | $0.080 | $0.280 | 100.00% | 100.00% | 99.7% | — | — | — |
| qwen3-vl-30b-a3b-instruct qwen/qwen3-vl-30b-a3b-instruct | deepinfra/fp8 | fp8 | 262k | $0.150 | $0.600 | 100.00% | 100.00% | 99.7% | — | — | — |
| deepseek-v3.2 deepseek/deepseek-v3.2 | deepinfra/fp4 | fp4 | 164k | $0.260 | $0.380 | 100.00% | 100.00% | 99.7% | — | — | 是 |
| mistral-small-3.2-24b-instruct mistralai/mistral-small-3.2-24b-instruct | deepinfra/fp8 | fp8 | 128k | $0.075 | $0.200 | 100.00% | 100.00% | 99.7% | — | — | — |
| qwen3.6-35b-a3b qwen/qwen3.6-35b-a3b | deepinfra/fp8 | fp8 | 262k | $0.100 | $0.950 | 100.00% | 100.00% | 99.7% | — | — | 是 |
| mimo-v2.6-flash xiaomi/mimo-v2.6-flash | deepinfra/fp8 | fp8 | 1.0M | $0.140 | $0.280 | 100.00% | 100.00% | 99.7% | — | — | 是 |
| qwen3-next-80b-a3b-instruct qwen/qwen3-next-80b-a3b-instruct | deepinfra/fp8 | fp8 | 262k | $0.090 | $1.10 | 100.00% | 100.00% | 99.6% | — | — | — |
| qwen3.5-35b-a3b qwen/qwen3.5-35b-a3b | deepinfra/fp8 | fp8 | 262k | $0.140 | $1.00 | 100.00% | 100.00% | 99.5% | — | — | 是 |
| glm-5.2 z-ai/glm-5.2 | deepinfra/fp4 | fp4 | 1.0M | $0.563 | $1.80 | 100.00% | 100.00% | 99.5% | — | — | 是 |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/fp8/priority | fp8 | 262k | $0.300 | $0.600 | 100.00% | 100.00% | 99.5% | — | — | — |
| gemma-4-31b-it google/gemma-4-31b-it | deepinfra/fp8/priority | fp8 | 262k | $0.300 | $0.600 | 100.00% | 100.00% | 99.5% | — | — | — |
| deepseek-v4.1-flash deepseek/deepseek-v4.1-flash | deepinfra/fp8/flex | fp8 | 1.0M | $0.112 | $0.336 | 100.00% | 100.00% | 99.4% | — | — | 是 |
| deepseek-v4.1-flash deepseek/deepseek-v4.1-flash | deepinfra/fp8/flex | fp8 | 1.0M | $0.112 | $0.336 | 100.00% | 100.00% | 99.4% | — | — | 是 |
| nemotron-3.5-lightning nvidia/nemotron-3.5-lightning | deepinfra/bf16 | bf16 | 262k | $0.060 | $0.160 | 100.00% | 100.00% | 99.1% | — | — | 是 |
| nemotron-3.5-lightning nvidia/nemotron-3.5-lightning | deepinfra/bf16 | bf16 | 262k | $0.060 | $0.160 | 100.00% | 100.00% | 99.1% | — | — | 是 |
| llama-guard-4-12b meta-llama/llama-guard-4-12b | deepinfra/bf16 | bf16 | 164k | $0.180 | $0.180 | 100.00% | 100.00% | 99.1% | — | — | — |
| qwen-2.5-72b-instruct qwen/qwen-2.5-72b-instruct | deepinfra/fp8 | fp8 | 33k | $0.360 | $0.400 | 100.00% | 89.3% | 99.1% | — | — | — |
| deepseek-v4.1-flash-1017 deepseek/deepseek-v4.1-flash-1017 | deepinfra/fp8/flex | fp8 | 1.0M | $0.078 | $0.235 | 100.00% | 100.00% | 98.8% | — | — | 是 |
| ling-3.0-flash inclusionai/ling-3.0-flash | deepinfra/bf16 | bf16 | 131k | $0.060 | $0.180 | 100.00% | 100.00% | 98.8% | — | — | 是 |
| gpt-oss-20b openai/gpt-oss-20b | deepinfra/bf16 | bf16 | 131k | $0.030 | $0.140 | 100.00% | 96.7% | 98.7% | — | — | — |
| gpt-oss-20b openai/gpt-oss-20b | deepinfra/bf16 | bf16 | 131k | $0.030 | $0.140 | 100.00% | 96.7% | 98.7% | — | — | — |
| gemma-3-27b-it google/gemma-3-27b-it | deepinfra/fp8 | fp8 | 131k | $0.080 | $0.160 | 100.00% | 100.00% | 98.7% | — | — | — |
| llama-3.1-70b-instruct meta-llama/llama-3.1-70b-instruct | deepinfra/turbo | fp8 | 131k | $0.320 | $0.320 | 0.0% | 75.0% | 98.5% | — | — | — |
| glm-5.3 z-ai/glm-5.3 | deepinfra/fp4 | fp4 | 1.0M | $0.563 | $2.50 | 100.00% | 100.00% | 98.4% | — | — | 是 |
| glm-5.3 z-ai/glm-5.3 | deepinfra/fp4 | fp4 | 1.0M | $0.563 | $2.50 | 100.00% | 100.00% | 98.4% | — | — | 是 |
| llama-guard-4-12b meta-llama/llama-guard-4-12b | deepinfra/bf16/priority | bf16 | 164k | $0.270 | $0.270 | 100.00% | 100.00% | 98.3% | — | — | — |
| qwen3-14b qwen/qwen3-14b | deepinfra/fp8 | fp8 | 41k | $0.120 | $0.240 | 0.0% | 67.9% | 98.2% | — | — | — |
| nemotron-3.5-super-vl nvidia/nemotron-3.5-super-vl | deepinfra/fp4 | fp4 | 262k | $0.150 | $0.500 | 100.00% | 100.00% | 98.1% | — | — | 是 |
| nemotron-3.5-super-vl nvidia/nemotron-3.5-super-vl | deepinfra/fp4 | fp4 | 262k | $0.150 | $0.500 | 100.00% | 100.00% | 98.1% | — | — | 是 |
| qwen3-vl-235b-a22b-instruct qwen/qwen3-vl-235b-a22b-instruct | deepinfra/fp8 | fp8 | 262k | $0.200 | $0.880 | 100.00% | 86.4% | 97.8% | — | — | 是 |
| granite-4.2-8b ibm-granite/granite-4.2-8b | deepinfra/bf16 | bf16 | 131k | $0.060 | $0.250 | — | 100.00% | 97.8% | — | — | 是 |
| deepseek-chat deepseek/deepseek-chat | deepinfra/fp4 | fp4 | 164k | $0.320 | $0.890 | 80.0% | 86.7% | 94.9% | — | — | — |
| gemma-3-12b-it google/gemma-3-12b-it | deepinfra/bf16 | bf16 | 131k | $0.050 | $0.150 | 100.00% | 100.00% | 94.5% | — | — | — |
| gemma-3-12b-it google/gemma-3-12b-it | deepinfra/bf16/priority | bf16 | 131k | $0.075 | $0.225 | 100.00% | 100.00% | 93.2% | — | — | — |
| qwen3-coder qwen/qwen3-coder | deepinfra/turbo | fp4 | 262k | $0.300 | $1.00 | 100.00% | 100.00% | 91.1% | — | — | 是 |
| qwen3-235b-a22b-2507 qwen/qwen3-235b-a22b-2507 | deepinfra/fp8 | fp8 | 262k | $0.090 | $0.550 | 100.00% | 100.00% | 83.3% | — | — | — |
| gpt-oss-120b openai/gpt-oss-120b | deepinfra/fp8 | fp8 | 131k | $0.200 | $0.950 | — | 100.00% | 82.8% | — | — | — |
| gpt-oss-120b openai/gpt-oss-120b | deepinfra/fp8 | fp8 | 131k | $0.200 | $0.950 | — | 100.00% | 82.8% | — | — | — |
| llama-4-scout meta-llama/llama-4-scout | deepinfra/fp8/priority | fp8 | 328k | $0.150 | $0.450 | 0.0% | 16.7% | 79.6% | — | — | — |
| gemma-3-12b-it google/gemma-3-12b-it | deepinfra/bf16/flex | bf16 | 131k | $0.040 | $0.120 | — | — | 14.3% | — | — | — |
| llama-4-scout meta-llama/llama-4-scout | deepinfra/fp8/flex | fp8 | 328k | $0.080 | $0.240 | — | — | 0.9% | — | — | — |
| phi-4 microsoft/phi-4 | deepinfra/bf16/flex | bf16 | 16k | $0.056 | $0.112 | — | — | — | — | — | — |
| llama-guard-4-12b meta-llama/llama-guard-4-12b | deepinfra/bf16/flex | bf16 | 164k | $0.144 | $0.144 | — | — | — | — | — | — |
| deepseek-chat deepseek/deepseek-chat | deepinfra/fp4/flex | fp4 | 164k | $0.256 | $0.712 | — | — | — | — | — | — |
| glm-4.6 z-ai/glm-4.6 | deepinfra/fp4/flex | fp4 | 203k | $0.400 | $1.60 | — | — | — | — | — | 是 |
覆盖模型 · 67
llama-3.1-8b-instructmistral-nemogpt-oss-120bl3-lunaris-8bgemma-3-4b-itmistral-small-24b-instruct-2501deepseek-v4-flash-0731gemma-4-26b-a4b-itphi-4qwen3-next-80b-a3b-instructnemotron-3-super-120b-a12bdeepseek-v4-flashdeepseek-v4.1-flash-1017qwen3.5-9bllama-4-scoutqwen3-30b-a3bdeepseek-v4.1-flashglm-5.3-flashqwen3.8-27bgemma-4-31b-itnemotron-3.5-content-safetyminimax-m3qwen3.5-27bmuse-glimmer-30bqwen3.6-27bglm-4.7mimo-v2.6-pronemotron-3-ultra-550b-a55binkling-smallglm-5.2deepseek-r1-0528kimi-k2.6hy4-previewl3.1-euryale-70bdeepseek-chat-v3.1deepseek-v4-pro-0813deepseek-v4-prokimi-k3ling-3.0-flash-vlqwen3.5-397b-a17bglm-4.6deepseek-v4-flash-vision-expllama-3.3-70b-instructling-3.1-flashqwen3-32bqwen3-vl-30b-a3b-instructdeepseek-v3.2mistral-small-3.2-24b-instructqwen3.6-35b-a3bmimo-v2.6-flashqwen3.5-35b-a3bnemotron-3.5-lightningllama-guard-4-12bqwen-2.5-72b-instructling-3.0-flashgpt-oss-20bgemma-3-27b-itllama-3.1-70b-instructglm-5.3qwen3-14b…另有 7 个
元数据与端点观测同步自 OpenRouter 公开 API。GrokCode 不做路由转发；自建测速请用 API 实验室。
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同系工具：Clavue CLI / imux IDE / clavue-2.1
Clavue · CLI 与 Agent 平台
CLI / GUI Agent 运行时、设备登录、会员额度与通用对话 API（api.clavue.com）。开发脚本、CI 与本地工具一条链路。
打开 Clavue ↗imux · 原生 macOS AI IDE
Clavue 平台的原生 IDE：多 Agent 分屏、Ghostty 级终端、Agent Chat、浏览器自动化与 Supervisor——不是又一个 Electron 壳。
了解 imux ↗Clavue 2.1 · 产品级大模型
旗舰模型 clavue-2.1（及 fast / pro / rev）：适合复杂推理与长程 Agent 循环。Chat、imux、API 共用会员额度。
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