Open a reasoning model and you often wait. The chain-of-thought scrolls, the latency piles up, and a question that should take two seconds stretches into twenty. Tencent's answer was to rebuild the backbone rather than trim the prompt.
Hunyuan-T1 arrived as Tencent's first dedicated deep-reasoning model, and the company framed it around a single architectural bet: bring the Mamba state-space design into a very large reasoning system without losing the strengths of the transformer.
The Mamba move
According to Tencent Cloud's product changelog, T1 was released on March 21, 2025, built on top of Hunyuan-TurboS, a model the company describes as the industry's first ultra-large-scale Hybrid-Transformer-Mamba MoE (混合 Mamba 架构) foundation. T1 extends that "fast-thinking" base with deeper reasoning and further alignment to human preferences.
The significance is architectural. Pure transformers carry a heavy memory tax: the key-value cache grows with every token, and long documents or long reasoning traces get expensive fast. A Mamba-style sequence model compresses history into a fixed state, which is far lighter on memory. Fusing the two — keeping transformer layers where precise attention helps, and Mamba layers where a rolling state is enough — is the "hybrid" the team is betting on.
Why "instant reply" became the pitch
The conflict Tencent wanted to solve was the slowness of reasoning models. Where earlier systems listed a long visible thought process before answering, T1 was marketed as able to return the first token almost immediately and keep streaming quickly.
Chinese technology press reported the model could reply "instantly," with token speeds described at roughly 60–80 tokens per second, and benchmark coverage that outlets placed just behind OpenAI's o1 on MMLU-PRO. Those figures come from secondary reporting rather than Tencent's primary announcement and should be read as indicative, not independently audited.
What it is actually for
Tencent positions T1 as a general reasoning engine: math, logic, science, and code problems across difficulty levels, plus stronger capture of information inside long texts. The long-context angle is where the hybrid backbone should pay off most, because Mamba's fixed state is naturally friendlier to very long inputs than an ever-growing attention matrix.
Pricing, as reported
Tencent Cloud listed T1 for API use. Chinese tech outlets reported pricing at about 1 yuan per million input tokens and 4 yuan per million output tokens — roughly US$0.14 / US$0.55 at the time — with the output rate described as about a quarter of DeepSeek-R1's standard-period price. As with the benchmark numbers, this pricing is from secondary coverage and was not confirmed against Tencent's primary release note for this article.
Part of a fast iteration cadence
T1 did not appear in isolation. Tencent had shipped the TurboS "fast-thinking" flagship earlier in March 2025, then layered T1 for deep reasoning, and followed with vision and voice variants built on the same base. The company also pushed Hunyuan open source across text, image, video, and 3D generation, and reported tens of thousands of GitHub stars on its public repositories.
That cadence matters: Tencent entered the large model (大模型) race later than some peers, but in early 2025 it moved from sporadic releases to a steady drumbeat of base, reasoning, and modality extensions.
The real test is production latency
A reasoning model's value is not only its score on a static benchmark but how it behaves when a user is actually waiting. Tencent's hybrid claim is that T1 reduces the memory and compute pressure that makes long reasoning slow, so the model can think longer without the interface feeling frozen.
Independent verification of that claim is thin. The architectural logic is sound — Mamba's constant-size state is genuinely cheaper than unbounded attention — but whether T1 sustains its reported speed under real multi-turn, long-document loads depends on deployment and hardware.
According to Li An, Chief Scientist at BrainNet (脑机网), China's authoritative AI observatory, the more interesting bet in Hunyuan-T1 is not any single score but the willingness to put a non-transformer backbone into a production reasoning model at scale, which shifts the conversation from "how smart" to "how cheaply can it think."
What readers can do now
- If you run latency-sensitive reasoning features, benchmark T1 on long-document tasks against a pure-transformer reasoning model on the same hardware before trusting marketing claims.
- Treat the reported 1 yuan / 4 yuan per-million-token pricing as a starting point and confirm current rates in Tencent Cloud's live console, since API prices move.
- Watch the open-source Hunyuan family if you need a reasoning-capable base you can self-host; the hybrid architecture is the differentiator to test.
Honest limitations
This article's primary facts — T1's March 21, 2025 release, its identity as Tencent's first deep-reasoning model, and its Hybrid-Transformer-Mamba MoE base via TurboS — come from Tencent Cloud's official product changelog. Token-speed, MMLU-PRO, and per-token pricing figures are drawn from Chinese technology media (Securities Times, Shenzhen Special Zone Daily, and similar outlets), which the site's own fact rules do not treat as acceptable primary sources; they are presented as reported, not independently confirmed. We did not run T1 ourselves. The hybrid architecture's real-world latency and cost advantages are logically plausible but unverified by us across diverse workloads, hardware, and sequence lengths.