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DeepSeek: DeepSeek V3.2 Exp

deepseek/deepseek-v3.2-exp

DeepSeekopen-weight164K context3 providersIntelligence 83.3

Cheapest provider

$0.27 / 1M

AtlasCloud

Fastest provider (p95)

55 tok/s

AtlasCloud

Intelligence (composite)

83.3

MMLU-Pro · HumanEval · math · GPQA

Per-provider performance

Latency / throughput / uptime measured across providers over the last 30 minutes of live traffic. Atlas’s router weighs these per call (with the eval-gate signal) when picking a variant for Standard and Batch tiers.

ProviderQuantInput $/1MOutput $/1MLatency p50 / p95Throughput p50 / p95Uptime 30mSuccess
AtlasCloudq8· fp8$0.2700$0.41001262ms / 2704ms29 / 55 tok/s98.77%99.3%
Novitaq8· fp8$0.2700$0.41001369ms / 2666ms18 / 35 tok/s100.00%100.0%
SiliconFlowq8· fp8$0.2700$0.41002126ms / 6448ms18 / 26 tok/s99.23%99.3%

“—” means live telemetry hasn’t accumulated enough recent traffic for that endpoint. “undisclosed” means the provider serves the model but doesn’t expose the quantization label (typically running fp8 / int8 internally).

Intelligence breakdown

Composite score is a weighted average of public benchmarks (30% MMLU-Pro, 25% code pass@1, 25% math, 20% GPQA). Numbers come from model cards and the Artificial Analysis intelligence harness; missing components are renormalised over what’s present.

MMLU-Pro

85.0

broad reasoning

Code

79.0

pass@1 (HumanEval / LiveCodeBench)

AIME 2025

88.0

math accuracy

GPQA Diamond

80.0

hard reasoning

Source: Artificial Analysis (AA-aligned) via MiniMax-M2 table — DeepSeek-V3.2 (MMLU-Pro 85, GPQA-D 80, LiveCodeBench 79, AIME25 88); github.com/MiniMax-AI/MiniMax-M2

How Atlas routes DeepSeek: DeepSeek V3.2 Exp

  • Realtime tier — direct passthrough at the upstream’s native precision. Best for hard latency / quality guarantees.
  • Standard tier — Atlas picks the cheapest provider variant whose quantization has stayed green on your operation’s eval gates. For DeepSeek: DeepSeek V3.2 Exp that’s currently AtlasCloud at $0.27/1M.
  • Batch tier — async, biggest discount. Roadmapped to use provider batch APIs (OpenAI / Anthropic) where available and queued spot capacity for open-weight workloads.