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LMSpeed is an AI model directory for comparing API price, output speed, first-token latency, provider coverage, and benchmark data. Use it to narrow your model and provider choice, then test the endpoint that fits your workload.
Price, speed, latency, and availability can change. Treat this table as a current signal and verify your own endpoint before you deploy.
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| Context131.1K | Input— | Output— | Providers |
Agents, Coding, Reasoning, and the other capability columns are 0–100 observed-capability estimates relative to the eligible model population in a dated Category Score V3 run.
They are not success rates, IQ scores, or an average across all eight categories. Read them with the 80% interval, measured dimensions, benchmark families, and evidence shown on each model page.
Each row brings together the information you need to compare an LLM API. Some fields are blank when LMSpeed has no current data for that model or provider.
Start with the decision that matters most for your workload, then compare the current rows before you test an endpoint.
LMSpeed runs standardized five-round API speed tests on each model, measuring output throughput (tokens per second), first-token latency, and total response time across multiple providers.
Latency varies by provider and model. Use the LMSpeed model directory to sort by latency and find the model with the fastest first-token response time. Check the latency leaderboard for monthly rankings.
LMSpeed lists input and output token prices per million for each model across available providers. Sort by price to find a lower-cost option, or filter by provider to compare rates side by side.
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28.3±13.9P |
34.6±11.1E |
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41.8±11.8E |
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| Throughput — | Latency — |
| Release date2025-10-17 |
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| GPT-5 Image MiniOpenAI | Context400K | Input— | Output— | Providers +3 | — | — | — | — | — | — | — | — | Throughput — | Latency — | Release date2025-10-16 |
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| Claude Haiku 4.5Anthropic | Context200K | Input$1.00/M | Output$5.00/M | Providers +184 | 42.8±16.2P | 47.4±11.2E | 47.9±11.1E | 47.1±16.0P | 45.7±16.1P | 53±16.1P | 58.1±16.1P | 53±16.3P | Throughput 102 t/s | Latency 2.77s | Release date2025-10-15 |
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| Qwen3 VL 8B InstructQwen | Context256K | Input$0.180/M | Output$0.700/M | Providers +2 | — | 35.9±13.9P | 41±11.1E | — | 41.5±16.1P | — | — | — | Throughput — | Latency — | Release date2025-10-14 |
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| GPT-5 ImageOpenAI | Context400K | Input— | Output— | Providers +4 | — | — | — | — | — | — | — | — | Throughput — | Latency — | Release date2025-10-14 |
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| O3 Deep ResearchOpenAI | Context200K | Input— | Output— | Providers +29 | — | — | — | — | — | — | — | — | Throughput — | Latency — | Release date2025-10-10 |
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| O4 Mini Deep ResearchOpenAI | Context200K | Input— | Output— | Providers +27 | — | — | — | — | — | — | — | — | Throughput — | Latency — | Release date2025-10-10 |
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| Gemini 2.5 Flash ImageGoogle | Context32.8K | Input— | Output— | Providers +85 | — | — | — | — | — | — | — | — | Throughput — | Latency — | Release date2025-10-07 |
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| Qwen3 VL 30B A3B InstructQwen | Context262.1K | Input$0.200/M | Output$0.800/M | Providers +1 | — | 46.1±13.9P | 47.8±11.1E | — | 49.8±16.1P | — | — | — | Throughput — | Latency — | Release date2025-10-06 |
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| GPT-5 ProOpenAI | Context400K | Input— | Output— | Providers +66 | — | — | — | — | — | — | — | — | Throughput — | Latency — | Release date2025-10-06 |
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| GLM-4.6Z.ai | Context202.8K | Input$0.550/M | Output$2.20/M | Providers +94 | 48.8±16.0P | 44.9±8.9 | 43.3±8.7 | 44.9±16.0P | 44.8±16.1P | — | — | 42.3±16.0P | Throughput 53 t/s | Latency 16.68s | Release date2025-09-30 |
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| Claude Sonnet 4.5Anthropic | Context1M | Input$3.00/M | Output$15.00/M | Providers +163 | 47.6±8.8 | 52.7±11.2E | 45.6±9.0E | 54.7±16.0P | 42.6±12.3E | 57.8±16.1P | 66.6±16.1P | 57±16.3P | Throughput 41 t/s | Latency 4.57s | Release date2025-09-29 |
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| DeepSeek V3.2 ExpDeepSeek | Context163.8K | Input$0.280/M | Output$0.420/M | Providers | — | 54±13.9P | 54.4±11.1E | — | 52.7±16.1P | — | — | — | Throughput — | Latency — | Release date2025-09-29 |
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| Gemini 2.5 Flash LiteGoogle | Context1.0M | Input$0.100/M | Output$0.400/M | Providers +112 | — | 37.1±13.9P | 43.1±11.1E | — | 53.3±11.8E | — | — | — | Throughput 277 t/s | Latency 1.41s | Release date2025-09-25 |
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| Qwen3 VL 235B A22B InstructQwen | Context131.1K | Input$0.700/M | Output$2.80/M | Providers +5 | — | 50.1±13.9P | 50.4±11.1E | — | 49.5±16.1P | — | — | — | Throughput — | Latency — | Release date2025-09-23 |
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| Qwen3 MaxQwen | Context262.1K | Input$1.20/M | Output$6.00/M | Providers +87 | 47.7±11.8E | 45.4±8.9 | 49.7±8.7 | 41.9±16.0P | 51.4±16.1P | — | — | 44.7±16.0P | Throughput 38 t/s | Latency 7.37s | Release date2025-09-23 |
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| Qwen3 Coder PlusQwen | Context1M | Input— | Output— | Providers +68 | — | — | — | — | — | — | — | — | Throughput 45 t/s | Latency 1.35s | Release date2025-09-23 |
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| GPT-5 CodexOpenAI | Context400K | Input$1.25/M | Output$10.00/M | Providers +121 | — | 56.6±13.9P | 57.2±11.1E | — | 54.7±16.1P | — | — | — | Throughput 154 t/s | Latency 2.76s | Release date2025-09-23 |
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| DeepSeek V3.1 TerminusDeepSeek | Context131.1K | Input$0.270/M | Output$1.00/M | Providers +83 | — | 55.6±13.9P | 54.6±11.1E | — | 53±16.1P | — | — | — | Throughput 58 t/s | Latency 1.50s | Release date2025-09-22 |
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| Qwen3 Next 80B A3B InstructQwen | Context262.1K | Input$0.500/M | Output$2.00/M | Providers +17 | — | 48.9±13.9P | 50.8±11.1E | — | 48.7±16.1P | — | — | — | Throughput — | Latency — | Release date2025-09-11 |
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| Kimi K2 0905MoonshotAI | Context262.1K | Input$0.600/M | Output$2.50/M | Providers— | — | 47.8±13.9P | 50.9±11.1E | — | 47.1±16.1P | — | — | — | Throughput — | Latency — | Release date2025-09-04 |
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| Kimi K2MoonshotAI | Context131.1K | Input$0.585/M | Output$2.40/M | Providers +82 | 45.6±16.0P | 45.7±11.1E | 49.3±8.7 | 48.5±14.0P | 53.7±9.3E | — | — | 43.9±16.0P | Throughput 29 t/s | Latency 2.21s | Release date2025-09-04 |
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| DeepSeek Chat V3.1DeepSeek | Context163.8K | Input— | Output— | Providers +6 | — | — | — | — | — | — | — | — | Throughput 23 t/s | Latency 2.02s | Release date2025-08-21 |
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| Mistral Medium 3.1Mistral | Context131.1K | Input$0.400/M | Output$2.00/M | Providers +2 | — | 46.6±13.9P | 43.2±11.1E | — | 43.6±16.1P | — | — | — | Throughput — | Latency — | Release date2025-08-13 |
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| GLM-4.5VZ.ai | Context65.5K | Input$0.600/M | Output$1.80/M | Providers +33 | — | 42.9±13.9P | 48.4±11.1E | — | 50±16.1P | — | — | — | Throughput 73 t/s | Latency 6.15s | Release date2025-08-11 |
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| GPT-5OpenAI | Context400K | Input$1.25/M | Output$10.00/M | Providers +146 | 45.8±9.3E | 51±8.9 | 51.9±8.7 | 53.6±14.0P | 61.5±11.8E | 57.8±16.1P | 64.9±16.1P | 53.2±16.0P | Throughput 88 t/s | Latency 6.23s | Release date2025-08-07 |
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| GPT-5 MiniOpenAI | Context400K | Input$0.250/M | Output$2.00/M | Providers +104 | — | 49.8±11.2E | 53.5±11.1E | — | 52.2±16.1P | — | — | — | Throughput 90 t/s | Latency 7.03s | Release date2025-08-07 |
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| GPT-5 NanoOpenAI | Context400K | Input$0.050/M | Output$0.400/M | Providers +103 | — | 51.7±13.9P | 48.1±11.1E | — | 50.9±16.1P | — | — | — | Throughput 123 t/s | Latency 10.60s | Release date2025-08-07 |
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| gpt-oss-120bOpenAI | Context131.1K | Input$0.150/M | Output$0.600/M | Providers +2 | 37.6±6.2 | 49.7±8.9 | 50.4±8.7 | 39.7±14.0P | 53.7±16.1P | 31.5±17.3P | 35.9±16.1P | 52.6±16.0P | Throughput — | Latency — | Release date2025-08-05 |
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| GPT-OSS | Context131.1K | Input— | Output— | Providers +127 | — | — | — | — | — | — | — | — | Throughput 390 t/s | Latency 2.28s | Release date2025-08-05 |
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| gpt-oss-20bOpenAI | Context131.1K | Input$0.060/M | Output$0.200/M | Providers | 38.3±9.3E | 44.8±8.9 | 44.7±8.7 | 30.9±14.0P | 52.9±16.1P | 40.8±16.1P | 31.3±16.1P | 51.3±16.0P | Throughput — | Latency — | Release date2025-08-05 |
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| Claude Opus 4.1Anthropic | Context200K | Input$15.00/M | Output$75.00/M | Providers +96 | 46.9±16.2P | 48.2±16.1P | 50.8±18.4P | 55.3±14.0P | — | 53.6±16.1P | 59.5±16.1P | 50.1±16.3P | Throughput 22 t/s | Latency 3.56s | Release date2025-08-05 |
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| Qwen3 Coder 30B A3B InstructQwen | Context160K | Input$0.450/M | Output$2.25/M | Providers | — | 43.5±13.9P | 43.1±11.1E | — | 50.5±11.8E | — | — | — | Throughput — | Latency — | Release date2025-07-31 |
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| GLM-4.5Z.ai | Context131.1K | Input— | Output— | Providers +74 | 46.6±16.5P | 51.8±13.9P | 53.1±11.1E | 36.7±16.0P | 60.6±11.8E | 39.8±16.1P | 31.3±16.1P | 35.8±16.3P | Throughput 39 t/s | Latency 7.22s | Release date2025-07-25 |
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| GLM-4.5 AirZ.ai | Context131.1K | Input$0.170/M | Output$0.980/M | Providers +88 | 43.1±16.0P | 47.2±11.1E | 47.8±8.7 | 31.8±14.0P | 56.9±11.8E | 39.8±16.1P | 31.3±16.1P | 42.6±16.0P | Throughput 60 t/s | Latency 10.99s | Release date2025-07-25 |
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| Qwen3 CoderQwen | Context1.0M | Input— | Output— | Providers +55 | — | — | — | — | — | — | — | — | Throughput 243 t/s | Latency 5.28s | Release date2025-07-23 |
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| Qwen3 235B A22B Instruct 2507Qwen | Context262.1K | Input$0.700/M | Output$8.40/M | Providers— | — | 56.2±13.9P | 54.1±11.1E | — | 63±11.8E | — | — | — | Throughput — | Latency — | Release date2025-07-21 |
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| Qwen3 | Context262.1K | Input$0.200/M | Output$0.800/M | Providers +120 | 46±16.5P | 46.4±13.9P | 48±11.1E | 36.9±16.3P | 58.4±11.8E | 36.7±16.4P | 31.3±16.1P | 55.7±16.3P | Throughput 100 t/s | Latency 12.55s | Release date2025-07-21 |
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| MiniMax M1MiniMax | Context1M | Input$0.550/M | Output$2.20/M | Providers +25 | — | 51.9±13.9P | 49.7±11.1E | — | 59±11.8E | — | — | — | Throughput — | Latency — | Release date2025-06-17 |
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| Gemini 2.5 FlashGoogle | Context1.0M | Input$0.300/M | Output$2.50/M | Providers +155 | 36.3±16.0P | 45.2±11.1E | 49.7±8.7 | 38.4±14.0P | 50.8±9.3E | 46.9±16.1P | 50.1±16.1P | 43.1±16.0P | Throughput 154 t/s | Latency 8.62s | Release date2025-06-17 |
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| Gemini 2.5 ProGoogle | Context1.0M | Input$1.25/M | Output$10.00/M | Providers +159 | 44.4±9.3E | 41.9±6.6 | 55.5±8.7 | 50.8±14.0P | 56.2±9.3E | 54.8±16.1P | 62.8±16.1P | 46.1±16.0P | Throughput 88 t/s | Latency 16.94s | Release date2025-06-17 |
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| O3 ProOpenAI | Context200K | Input$20.00/M | Output$80.00/M | Providers +57 | 58±16.5P | 49.9±17.0P | 54.9±16.0P | 59.4±14.0P | — | 53.6±16.1P | 54.4±16.1P | 49.1±16.3P | Throughput — | Latency — | Release date2025-06-10 |
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| Gemini 2.5 Pro Preview 06-05Google | Context1.0M | Input$1.25/M | Output$10.00/M | Providers— | — | 54.7±13.9P | 55.9±11.1E | — | 60.8±11.8E | — | — | — | Throughput — | Latency — | Release date2025-06-05 |
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| DeepSeek R1DeepSeek | Context163.8K | Input$1.35/M | Output$4.20/M | Providers +143 | 41.4±16.0P | 46.4±11.1E | 51±8.7 | 43.2±14.0P | 57.1±11.8E | 41.4±16.1P | 35.2±16.1P | 43.3±16.0P | Throughput 52 t/s | Latency 10.36s | Release date2025-05-28 |
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| Claude Opus 4Anthropic | Context200K | Input$15.00/M | Output$75.00/M | Providers +85 | — | 51.7±13.9P | 51.9±11.1E | — | 54.4±11.8E | — | — | — | Throughput 25 t/s | Latency 12.05s | Release date2025-05-22 |
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| Claude Sonnet 4Anthropic | Context1M | Input$3.00/M | Output$15.00/M | Providers +115 | 44.6±9.3E | 48±8.9 | 48.2±8.7 | 48.7±14.0P | 52.9±11.8E | 54.2±16.1P | 58.8±16.1P | 45.1±16.0P | Throughput 34 t/s | Latency 4.93s | Release date2025-05-22 |
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| Mistral Medium 3Mistral | Context131.1K | Input$0.400/M | Output$2.00/M | Providers +3 | 38.9±16.0P | 38.4±11.1E | 42.6±8.7 | 48.1±14.0P | 52.1±11.8E | — | — | 43.2±16.0P | Throughput — | Latency — | Release date2025-05-07 |
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| Qwen3 30B A3BQwen | Context131.1K | Input$0.200/M | Output$2.40/M | Providers | — | 41.6±13.9P | 43.4±11.1E | — | 49.3±11.8E | — | — | — | Throughput — | Latency — | Release date2025-04-28 |
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| Qwen3 8BQwen | Context131.1K | Input$0.180/M | Output$2.10/M | Providers | — | 33.3±13.9P | 39.9±11.1E | — | 48.4±11.8E | — | — | — | Throughput — | Latency — | Release date2025-04-28 |
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| Qwen3 14BQwen | Context131.7K | Input$0.350/M | Output$4.20/M | Providers | — | 41±13.9P | 41.7±11.1E | — | 49.7±11.8E | — | — | — | Throughput — | Latency — | Release date2025-04-28 |
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| Qwen3 32BQwen | Context131.1K | Input$0.700/M | Output$8.40/M | Providers | — | 42±13.9P | 44.1±11.1E | — | 49.9±11.8E | — | — | — | Throughput — | Latency — | Release date2025-04-28 |
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| Qwen3 235B A22BQwen | Context131.1K | Input$0.700/M | Output$8.40/M | Providers | — | 43.8±13.9P | 46.2±11.1E | — | 51±11.8E | — | — | — | Throughput — | Latency — | Release date2025-04-28 |
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| o4 Mini HighOpenAI | Context200K | Input— | Output— | Providers— | 53.9±16.5P | 47.7±17.0P | 49±18.4P | 49.5±16.0P | 51.7±16.1P | 54.2±16.1P | 47.6±16.1P | 50.1±16.3P | Throughput — | Latency — | Release date2025-04-16 |
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| O4 MiniOpenAI | Context200K | Input$1.10/M | Output$4.40/M | Providers +82 | — | 59.6±13.9P | 53.8±11.1E | — | 64±11.8E | — | — | — | Throughput 94 t/s | Latency 8.08s | Release date2025-04-16 |
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| O3OpenAI | Context200K | Input$2.00/M | Output$8.00/M | Providers +79 | 49.8±16.0P | 55.1±11.1E | 55±8.7 | 50.9±14.0P | 58.9±9.3E | 53.6±16.1P | 53.2±16.1P | 53.5±16.0P | Throughput 134 t/s | Latency 2.82s | Release date2025-04-16 |
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| GPT-4.1OpenAI | Context1.0M | Input$2.00/M | Output$8.00/M | Providers +103 | 39.9±11.8E | 39.3±8.9 | 49.2±8.7 | 57±17.4P | 49±9.3E | 46.9±16.1P | 54.1±16.1P | 42.1±11.9E | Throughput 85 t/s | Latency 1.92s | Release date2025-04-14 |
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| GPT-4.1 MiniOpenAI | Context1.0M | Input$0.400/M | Output$1.60/M | Providers +105 | 41.8±11.8E | 37.6±8.9 | 45.9±8.7 | 49.3±17.4P | 49.9±9.3E | 48.7±16.1P | 51.3±16.1P | 42.4±11.9E | Throughput 93 t/s | Latency 2.69s | Release date2025-04-14 |
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| GPT-4.1 NanoOpenAI | Context1.0M | Input$0.100/M | Output$0.400/M | Providers +96 | 33.7±11.8E | 36±11.1E | 38±8.7 | 33.7±17.4P | 45.1±9.3E | 40.8±16.1P | 43.6±16.1P | 37±11.9E | Throughput 140 t/s | Latency 1.78s | Release date2025-04-14 |
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| Llama 4 MaverickMeta | Context1.0M | Input$0.350/M | Output$0.850/M | Providers +14 | 33.3±11.8E | 39.4±11.1E | 47.2±8.7 | 36.1±14.0P | 46.1±9.3E | 40.2±16.1P | 42.3±16.1P | 44.3±16.0P | Throughput — | Latency — | Release date2025-04-05 |
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| Llama 4 ScoutMeta | Context10M | Input$0.175/M | Output$0.625/M | Providers +35 | 33.9±11.8E | 32.9±11.1E | 42±8.7 | 34.5±14.0P | 44.3±9.3E | 40.2±16.1P | 43±16.1P | 43.2±16.0P | Throughput 75 t/s | Latency 1.62s | Release date2025-04-05 |
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An AI model directory is a searchable list of models and their comparison data. On LMSpeed, it brings together API price, throughput, first-token latency, provider coverage, and capability data when available.
Start with the metric that matters most for your workload. Then open a model page to compare provider data. Run your own speed test before you use an endpoint in production.
Yes. Price, availability, throughput, and latency can change by model, provider, and time. Use current page values as a comparison signal and confirm with your own endpoint test.
