Everyone is interested in the fact that GPT 5.6 sol output speed is 12 times faster than Kimi K3.

Everyone is interested in the fact that GPT 5.6 sol output speed is 12 times faster than Kimi K3. Let me list the relevant materials to read together: Group 1: Where does Sol's 750 tok/s come from?

Cerebras Runs OpenAI GPT-5.6 Sol at 750 Tokens per Second — Value Add VC (Month 26) The earliest first-hand report corresponds to the official announcement date of Cerebras. Read it first to establish the timeline: 750 t/s is the release commitment value, not the third-party measurement.
36kr: GPT-5.6 Inference Rush 750 Tokens/s (Month 9, Chinese) cited the original OpenAI official blog post, confirming that 750 t/s is a number written by OpenAI itself, and is on the premise of Cerebras custom hardware.
Vectrel: Inference Speed Is the New AI Frontier (13 July) confirms that this speed is realistically available after July 9 GA and points to the key premise of "roughly an order of magnitude faster than typical GPU inference" - a 12x source benchmark. Group 2: Where does K3's 62 tok/s come from?
Artificial Analysis: Kimi K3 Providers (primary reference source, referenced in the following two secondary articles. Note when reading it: 62 t/s is the measured average of multiple API service providers, not the official nominal value.
GLM
2 vs Kimi K3 — GLM-5 AI (gives the clearest comparison table: K3 outputs 62 t/s, TTFT 1.99 s, and explains that "K3 is a reasoning model, TTFT contains a chain of thought".
BenchLM: GPT-5.2 vs Kimi K3 (cross-validation 62 t/s) This number is consistent in multiple comparison pages, and the context is given at the same time: GPT series model TTFT is generally very long (30s +). Group 3: Why is it so bad (hardware layer explanation)?
Tiger Sniff: Cerebras wafer-level chip (most popular: WSE-3 gives half of the chip area to 44GB of on-chip SRAM with a bandwidth of 21 PB/s, "about a thousand times that of GPU HBM".
How Cerebras AI Chips Differ from NVIDIA GPUs — Mental Momentum (Technical Details: 900,000 cores, 48KB SRAM per core, 21 PB/s aggregate bandwidth, about 2600 times compared to B200.
Kimi K3 official technical blog (+ i-SCOOP's K3 interpretation (see K3 side:
8T MoE, MXFP4 quantization, official recommendation for 64 + card hypernode deployment, cache hit rate > 90% - understand its design orientation of "taking the cost route rather than the speed route".

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