DeepSeek-V4-Pro Gets Refreshed: DeepSeek’s flagship only slightly outperforms Flash model; open source harness draws interest

Developers who already track models’ cost and accuracy have good new reasons to pay closer attention to a third essential factor: speed.

What’s new: OpenAI and Cerebras previewed Ultrafast, a new API service tier that runs GPT-5.6 Sol on Cerebras hardware instead of OpenAI’s usual infrastructure.

• Throughput: Ultrafast touts up to 750 output tokens per second. Artificial Analysis measures GPT-5.6 Sol at max reasoning on Open AI’s own API at 65 tokens per second, which would make Ultrafast about 11 times faster.
• Time to finish: Across six quality-matched GDPVal tasks (OpenAI’s benchmark of realistic, economically valuable work tasks), Cerebras measured Ultrafast at 83 seconds per task against 7.7 minutes for standard Sol, a 5.6x end-to-end speedup.
• Latency: GPT-5.6 has an unusually high latency. Artificial Analysis lists standard GPT-5.6 Sol at max reasoning at 97.2 seconds to its first answer token, against 14.5 seconds for GPT-5.5 at high reasoning. Neither company has published a latency figure for Ultrafast.
• Price Availability: Limited preview for selected customers, waitlist for everyone else, no price or general-availability date announced
• Undisclosed: Independent confirmation of benchmark figures

Two other releases landed the same week with speed as their headline pitch. Google shipped Gemini 3.7 Flash, and Nvidia released Nemotron 3.5 Lightning. All three are aimed at developers building applications where response time determines how well a product works.

What is speed, exactly?: Two measurements often show up under the word “speed” in AI, and different models and hardware handle them differently. Latency, sometimes called time-to-first-token, is the wait before a model’s response arrives; throughput is how many tokens per second it produces once it starts. Ultrafast targets throughput: Cerebras’ hardware keeps GPT-5.6 Sol’s weights in 44 gigabytes of on-chip SRAM, avoiding the trips to external memory that create bottlenecks on GPU-based inference.

• GPT-5.6 Sol on Ultrafast: The clearest throughput demonstration comes from Cerebras. Ultrafast answered all 2,500 questions on Humanity’s Last Exam, a test of expert-level questions across more than 100 subjects, in 11 hours and 11 minutes, compared to 78 hours and 27 minutes for Claude Fable 5, that is, about 7 times faster. Cerebras also reports Ultrafast running 11 times faster than Claude Fable 5 and 5 times faster than Claude Opus 4.8 on Fast mode overall.
• Gemini 3.7 Flash: Google hasn’t disclosed throughput or latency figures for Gemini 3.7 Flash, but Artificial Analysis measured 330 output tokens per second — only behind Gemini 3.5 Flash-Lite — and a 13.2-second wait to the first token. The model also posts performance gains measured against its predecessor: Its Artificial Intelligence Index average is 56, up from 52 for Gemini 5.6 Flash.
• Nemotron 3.5 Lightning: Up to 4 times faster output than other models in its class and 30 percent faster agentic task completion, according to Nvidia. Artificial Analysis measured a throughput average of 302 output tokens per second across providers, with a 7.5 second time to first token. Nvidia’s NeMo Switchyard, an open-source routing library released alongside the model, sends each step of an agentic workflow to whatever available model best fits that step’s needs for speed, quality, or cost. Nvidia reports that routing through Switchyard, rather than running every step on a single large model, cut task-completion cost to about a third of the cost of Claude Opus 4.8 alone.

Behind the news: Ultrafast isn’t OpenAI’s only investment in speed. This week, the company released its first test results for Jalapeño, its new inference chip produced in partnership with Broadcom. These tests show promising latency and throughput measurements when running both OpenAI’s and open weights models. Note that chipmakers also have to balance speed against the total number of users and power used by their hardware: Each affects cost and reliability of inference.

Why it matters: Inference at these speeds changes what a model can be used for rather than just how well it performs a fixed task.

• In conversational and other real-time applications, users notice latency directly: A voice assistant that pauses for more than about a second starts to feel broken, because human conversation runs on turn gaps of roughly 0.3 to 1 second.
• For developers using agents to write code, a few minutes of wait time for a result is long enough to get distracted, lose your place, or start another task. Coming back later means rebuilding the problem in your head, which increases mental fatigue from context switching.
• Low latency and high throughput help always-on agents monitor a system, a website, or a security feed and react as events happen. An agent that takes one minute of analysis to notice a problem potentially already missed the window to act on it in real time.
• Tool use is also highly latency- and throughput-dependent. Search, code execution, and data retrieval tools all add time to agentic workflows.

We’re thinking: Faster AI models have already enabled agents that code, retrieve data, and respond to conversations at the speed of speech. We encourage developers not only to think about which of their applications require more throughput and less latency, but new applications that are only possible with the kinds of speed new models and hardware can deliver.