PRICE~moonshotai/kimi-latest cached_input_per_mtok decreased from 0.80 to 0.291mPRICE~moonshotai/kimi-latest output_per_mtok decreased from 13.00 to 11.361mPRICE~moonshotai/kimi-latest input_per_mtok decreased from 0.99 to 0.751mPRICE~deepseek/deepseek-pro-latest cached_input_per_mtok increased from 0.0042 to 0.101mPRICE~deepseek/deepseek-pro-latest output_per_mtok increased from 0.40 to 4.201mPRICE~deepseek/deepseek-pro-latest input_per_mtok decreased from 0.13 to 0.131mPRICE~moonshotai/kimi-latest cached_input_per_mtok increased from 0.29 to 0.8032mPRICE~moonshotai/kimi-latest output_per_mtok increased from 11.36 to 13.0032mPRICE~moonshotai/kimi-latest input_per_mtok increased from 0.75 to 0.9932mPERFORMANCEGLM-5.2 → Scaleway: TTFT ↑ 20%1hPERFORMANCEDeepSeek V4 Flash → Scaleway: TTFT ↑ 35%1hPERFORMANCEMistral Medium 3.5 → Scaleway: TTFT ↓ 21%1hPERFORMANCEDeepSeek V4 Flash → Scaleway: throughput ↓ 20%1hPERFORMANCEGPT-OSS 120B → Scaleway: throughput ↑ 31%1hPERFORMANCEGPT-OSS 120B → Scaleway: TTFT ↓ 24%1hPERFORMANCEGPT-OSS 120B → Together AI: throughput ↑ 20%1hPERFORMANCEKimi K3 via OpenRouter: TTFT ↓ 49%1hPERFORMANCEGLM-5.2 via OpenRouter: reliability recovered 96.8% → 100.0%1hPERFORMANCEKimi K3 via Cortecs: throughput ↑ 184%1hPERFORMANCEKimi K3 via Cortecs: TTFT ↓ 42%1hPERFORMANCEGLM-5.2 via OpenRouter: throughput ↓ 49%1hPERFORMANCEGLM-5.2 via OpenRouter: TTFT ↑ 121%1hPERFORMANCEMiniMax M3 via OpenRouter: reliability recovered 96.8% → 100.0%1hPERFORMANCEDeepSeek V4 Pro via Cortecs: throughput ↑ 27%1hPERFORMANCEGPT-OSS 120B via OpenRouter: throughput ↓ 22%1hPERFORMANCEGLM-4.7 via OpenRouter: TTFT ↓ 78%1hPERFORMANCEGPT-OSS 20B → Groq: TTFT ↑ 24%1hPERFORMANCEKimi K3 → Together AI: TTFT ↓ 34%1hPERFORMANCEDeepSeek V4 Flash via OpenRouter: throughput ↓ 17%1hPERFORMANCELlama 3.3 70B via Cortecs: TTFT ↓ 51%1hPERFORMANCEQwen3 235B via OpenRouter: throughput ↑ 37%1hPERFORMANCEQwen3 235B via OpenRouter: TTFT ↓ 29%1hPERFORMANCEDeepSeek V4 Flash → Together AI: throughput ↑ 19%1hPERFORMANCEGLM-5.3 via OpenRouter: throughput ↓ 17%1hPERFORMANCEGPT-OSS 20B via Cortecs: throughput ↑ 28%1hPERFORMANCELlama 3.3 70B → Together AI: TTFT ↓ 33%1hPRICE~moonshotai/kimi-latest cached_input_per_mtok decreased from 0.80 to 0.291hPRICE~moonshotai/kimi-latest output_per_mtok decreased from 13.00 to 11.361hPRICE~moonshotai/kimi-latest input_per_mtok decreased from 0.99 to 0.751hPRICE~deepseek/deepseek-v4-flash-latest cached_input_per_mtok decreased from 0.0077 to 0.00111h
BENCHMARK

Same Model, Different Infrastructure

The identical open-weight model, measured across every provider that serves it.

Key findings
  • GPT-OSS 120B spans 179–474 ms median TTFT across measured providers — a 2.6× spread on identical weights.
  • Measured reliability on the same model ranges 0.0%–100.0%.
  • Output price for the same tokens spans $0.17–$0.75 per million.

One model, many products

GPT-OSS 120B is the widest-covered model in the measured set (6 providers). Whatever explains the differences — hardware, batching policy, routing, quantisation — the buyer experiences them as different products at different prices wearing the same name.

TTFT179 ms474 ms
Throughput68 tok/s1749 tok/s
Output price$0.17/M$0.75/M
Reliability93.5%100.0%
Measured ranges on GPT-OSS 120B: TTFT, reliability, tools, price · ● MEASURED
What this means for engineers
  • Never extrapolate a model's behaviour from one provider's serving of it.
  • Model benchmarks without an infrastructure axis hide the variable that dominates production experience.
Dataset

Figures recompute from the live rolling window on every visit; the snapshot and versions above are the provenance of the regime that produced them. View the current benchmark →

Cite this researchInferenceBench (2026). Same Model, Different Infrastructure. InferenceBench Research.
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