Hy3 vs GPT-5.5: The $0.80 Apache Agent vs The $30 Proprietary Giant

Two Radically Different Philosophies

Tencent Hy3 (July 6, 2026) is an open-weight 295B Mixture-of-Experts model under Apache 2.0 — 21B active parameters, 192 routed experts, $0.20/$0.80 per 1M tokens (or $0.063/$0.21 on preview pricing). Built in under 90 days, hardened through 50+ internal Tencent products, it prioritizes reliability: hallucination rates dropped 57%, commonsense errors halved. It's available on Hugging Face, ModelScope, and GitHub.

OpenAI GPT-5.5 (April 23, 2026) is OpenAI's flagship general-purpose model — proprietary, $5/$30 per 1M tokens (standard), $30/$180 (Pro tier). It features a 1M-token context window (4× Hy3's 256K), explicit chain-of-thought reasoning, and ships inside ChatGPT, Codex CLI, and the OpenAI API. It holds the #1 non-Mythos position on Terminal-Bench 2.1 and ranks #3 on SWE-bench Verified behind only Claude Fable 5 and Opus 4.8.

Coding: The Gap Is Real

The SWE-bench Pro near-tie (57.9 vs 58.6) is the most surprising result. On the harder, more realistic benchmark with 1,865 tasks across 41 repos, Hy3 is statistically tied with GPT-5.5. But DeepSWE tells the opposite story: GPT-5.5's 70% vs Hy3's 28% is a 42-point chasm. DeepSWE's tasks require 5.5× more code than SWE-bench Pro with shorter prompts — closer to real engineering work. GPT-5.5's lead here is decisive.

For Terminal-Bench 2.1, GPT-5.5 (83.4% via Codex CLI) leads Hy3 (71.7%) by 11.7 points. However, note the harness matters: GPT-5.5 drops to 78.2% on the standardized Terminus 2 harness. Hy3's score is vendor-reported without a public harness, so direct comparison should be treated as directional.

Agents & Search: Hy3's Counterpunch

* Not published by the other vendor

Hy3 wins MCP Atlas by 3.8 points (79.1% vs 75.3%) — the #3 score overall and #1 among open-weight models. This measures multi-server tool orchestration, the core of autonomous agent reliability. Hy3 also edges GPT-5.5 on HLE with tools (+1.0), suggesting its tool-use capabilities hold up under the hardest reasoning tasks.

The BrowseComp near-tie (84.2 vs 84.4) is the story-within-the-story: on OpenAI's own agentic browsing benchmark, a $0.80/1M open-weight model is 0.2 points behind a $30/1M proprietary model. The gap has effectively closed.

Reasoning & STEM

GPT-5.5 leads on GPQA Diamond (+3.2) and HLE no-tools (+4.4). But Hy3 brings unique strengths: IMOAnswerBench 90.0% and USAMO 2026 72.0% suggest strong mathematical reasoning. FrontierMath T1-3 (51.7% for GPT-5.5) has no Hy3 equivalent published — Tencent didn't evaluate on it.

The Economics: 37.5× Output Price Gap

For 100M output tokens/month: Hy3 costs $80. GPT-5.5 costs $3,000. That's the difference between a hobby project and a venture-funded startup. Even at GPT-5.5's cached input rates ($0.50/1M), Hy3's uncached rate is cheaper.

And there's the reasoning token multiplier: GPT-5.5's chain-of-thought tokens bill as output. Complex coding tasks can generate 3-10× more reasoning tokens than visible output. Hy3 supports configurable reasoning levels (disabled/low/high), letting you dial the thinking budget per request.

The Self-Hosting & Ecosystem Factor

GPT-5.5 cannot be self-hosted. It lives inside OpenAI's API and ChatGPT. Hy3, under Apache 2.0, can run on your own hardware — 295B total / 21B active fits 2× DGX Spark or 8× H20 GPUs via vLLM or SGLang. For regulated industries, defense contractors, or anyone who needs air-gapped deployment, this alone may be the deciding factor.

GPT-5.5 counters with unmatched ecosystem depth: Codex CLI, ChatGPT, OpenAI API, Azure, and an army of third-party tooling. Hy3's ecosystem is smaller but growing — OpenRouter, SiliconFlow, and GMICloud all host it, and the Apache 2.0 license means the community can build around it freely.

12-Point Verdict

Which One?

Choose GPT-5.5 if:

• Coding is your primary workload. +10.7 Verified, +11.7 TB 2.1, +42 DeepSWE are not small gaps.
• You need 1M context. For codebase-scale reasoning across thousands of files.
• You want the OpenAI ecosystem. Codex CLI, ChatGPT, Azure, third-party integrations everywhere.
• You need frontier math. FrontierMath T4 at 35.4% has no open-weight peer.

Choose Hy3 if:

• Cost matters at scale. 37.5× cheaper output, 25× cheaper input. At production volume, this is six figures vs six figures.
• You need to self-host. Apache 2.0, fits consumer hardware, no vendor lock-in.
• You're building agents. MCP Atlas #3 overall, BrowseComp near-tie, DeepSearchQA 91.0%.
• You want predictable economics. No reasoning-token surprises, configurable thinking budget.

The Bottom Line

GPT-5.5 is the better coder — by a margin that ranges from near-tie (SWE-bench Pro) to canyon (DeepSWE). Hy3 is the better value agent — winning MCP Atlas, near-tying BrowseComp, and doing it all at 1/37th the price. The BrowseComp near-tie (84.2 vs 84.4) is the canary: on agentic web tasks, open-weight has caught proprietary. On deep coding, the gap remains real. Pick your weapon accordingly.

Sources: Tencent Hy3 · OpenAI GPT-5.5 · Vellum · Terminal-Bench 2.1 · DeepSWE · Snorkel AI · 36Kr. Vendor-reported scores unless noted.