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
ENGINEERING STUDY

Tool Calling Is an Infrastructure Property

The same model's function-calling reliability varies wildly with the provider serving it.

Key findings
  • On DeepSeek V4 Flash, measured tool-call reliability spans 0.0%–100.0% across 5 providers serving identical weights.
  • OpenRouter leads the measured set on this model; the weakest path fails roughly 100 of every 100 tool calls.
  • Identical weights, different serving stacks, different tool-call outcomes: the split is infrastructure-side.

Where tool calls break

A failed tool call is rarely the model refusing — it is malformed JSON, truncated arguments, or a serving stack that mangles the function-calling protocol under load. That is infrastructure behaviour, and it only becomes visible when the same model is measured across providers under an identical harness.

ProviderTool reliability
OpenRouter100.0%
Cortecs100.0%
Together100.0%
Scaleway100.0%
Fireworks0.0%
Measured tool-call reliability on DeepSeek V4 Flash (Agents profile) · ● MEASURED
What this means for engineers
  • For agent stacks, provider selection is a first-order reliability decision — test the exact path, not the model in the abstract.
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). Tool Calling Is an Infrastructure Property. InferenceBench Research.
← All research
Related research
Tool Calling Is an Infrastructure Property · InferenceBench