Grok vs Hy3
Snappy + real-time — the X-native model. vs Tencent's open-weights coder — Apache-2.0, cheap, beats GLM-5.1 on frontend in Tencent's blind eval.
Head-to-head verdict: Grok wins 4–1 with 1 tie.
What I tested — same prompt, two models
I run the same fixed prompt set through every new model the day it drops — same string, one shot, single HTML file out — and I score the result 0–10 on whether it ran, how close it hit the brief, and how good it looked. Below is what came out when I gave the exact same prompts to Grok and Hy3, side by side, on 7 shared tasks inside the Agent Operating System.
Both models were given identical prompts inside the Agent Operating System — no help, no iteration, no "best of N" tricks. I run each prompt once, save the HTML file the model produces, and score it 0–10 on whether it ran, how close it hit the brief, and how good it looked. The scoring is mine. The verdicts below are pulled from my source comparison guides at agentos.guide where I publish every score and the reasoning behind it.
Grok · Used for real-time content workflows where the model needs current X timeline context. Standalone bench scoring pending.
Hy3 · Wired into the Agent OS as the 'Hy3 Coder' tab (chat + live preview + workspace) via OpenRouter. Bench built one-shot on the same prompts as the field; weak builds iterated by Hy3 itself (the model fixes its own builds).
Side-by-side on 47 shared tasks
Click any cell to play that model's actual one-shot attempt. Medals are derived from my 0–10 scores per task (highest = 🥇, second = 🥈, third = 🥉).
Where Grok beat Hy3
The tasks where I gave Grok a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
What I saw: Doom-style FPS with sprite enemies, gun + muzzle flash + ammo/health HUD, textures, pointer-lock mouse-look. 22KB.
What I saw: 10KB · plays clean · three, webgl, input (re-rolled)
What I saw: 9KB · plays clean · three, webgl, input, rAF
What I saw: 11KB · plays clean · three, webgl, input
Where Hy3 beat Grok
The tasks where I gave Hy3 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
What I saw: Clean multi-scene pipeline with animated count-up stats, gold/cyan cinematic palette, particle field and progress bar render correctly; but the stats appear left-clustered and off-center with only two of four visible mid-animation, feeling sparse rather than composed, keeping it …
Strengths & weaknesses I logged
Grok
Strengths
- Real-time access to X timeline data — unique signal no other model has
- Snappy latency on shorter prompts
- 256K context window keeps pace with the open-weights field
Trade-offs
- 13 demos on the bench but zero have curated 0–10 verdicts yet — currently unranked
- API access is gated behind X Premium, awkward for backend agent loops
Hy3
Strengths
- Apache-2.0 open weights — self-host free, no lock-in
- Tencent's 270-expert blind eval: 2.67/4 vs GLM-5.1's 2.51, strongest on frontend / data / CI-CD
- Hallucination rate cut 12.5% → 5.4%; stable tool-calls across scaffoldings (<4% SWE-Bench variance)
Trade-offs
- Slow upstream on OpenRouter (30-90s per build) — fine for one-shots, sluggish for tight loops
- One-shot game builds can under-render (flat raycaster walls, unlit 3D) without an iterate pass
Pricing & context — the spec sheet
| Spec | Grok | Hy3 |
|---|---|---|
| Vendor | xAI | Tencent Hunyuan |
| Context window | 256,000 tokens | 262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter. |
| Price | Subscription via X Premium | $0.14 / 1M input · $0.58 / 1M output |
| Pricing detail | Bundled with X (Twitter) Premium subscription — no per-token bill for end users, no individual API pricing for the chat product. | Tencent Hunyuan 3 — open-weights under Apache-2.0, so free to self-host. On OpenRouter it is one of the cheapest capable coders: ~$0.14/M in, $0.58/M out (1 RMB / 4 RMB). Upstream can be slow (30-90s to first token), but per-token cost is negligible. |
| Release | 2026-04 | 2026-07-06 |
| Bench coverage | 43/47 scored · avg 8.09/10 | 7/7 scored · avg 7.13/10 |
The verdict — which should you pick?
Across 6 scored shared tasks, Grok averaged 7.92/10, beating Hy3's 7.12/10 by 0.80 points. Pick Grok when the build has to ship on the first prompt and you can afford the trade-offs in the comparison below.
If you only run one of these inside your stack, the head-to-head average above is the call. If you can run both, my honest play is to wire Grok and Hy3 both into the Agent Operating System and dispatch each from the kanban by task type — workflows that need live x / twitter context → Grok, cost-sensitive coding + frontend design where open weights matter → Hy3. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Grok vs Hy3
Which is better, Grok or Hy3?
On Goldie Bench, Grok averages 7.92/10 across the shared tasks, with 5 gold, 7 silver, 7 bronze overall. Hy3 averages 7.12/10, with 0 gold, 1 silver, 0 bronze. Grok wins the head-to-head 4–1.
How much does Grok cost vs Hy3?
Grok: Bundled with X (Twitter) Premium subscription — no per-token bill for end users, no individual API pricing for the chat product. Hy3: Tencent Hunyuan 3 — open-weights under Apache-2.0, so free to self-host. On OpenRouter it is one of the cheapest capable coders: ~$0.14/M in, $0.58/M out (1 RMB / 4 RMB). Upstream can be slow (30-90s to first token), but per-token cost is negligible.
What's the context window for Grok vs Hy3?
Grok has a 256,000 tokens context window. Hy3 has a 262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter. context window.
When should I pick Grok over Hy3?
Pick Grok for: Workflows that need live X / Twitter context; Snappy prompts where latency matters; Researchers comparing X-native models against the rest of the field. The trade-off is the weaknesses we logged on the bench: 13 demos on the bench but zero have curated 0–10 verdicts yet — currently unranked; API access is gated behind X Premium, awkward for backend agent loops.
When should I pick Hy3 over Grok?
Pick Hy3 for: Cost-sensitive coding + frontend design where open weights matter; Self-hosters who want an Apache-2.0 model they fully own; Anyone wiring a cheap capable coder into a live build panel (Agent OS Hy3 Coder tab). The trade-off is the weaknesses we logged on the bench: Slow upstream on OpenRouter (30-90s per build) — fine for one-shots, sluggish for tight loops; One-shot game builds can under-render (flat raycaster walls, unlit 3D) without an iterate pass.
How does Goldie Bench score Grok vs Hy3?
Every demo on this page was built by Julian Goldie inside the Agent Operating System — same fixed prompt for both models, one shot, single HTML file out. Each result gets a 0–10 score on whether it ran, how close it hit the brief, and how good it looked. The highest score on each task gets gold; second gets silver; third gets bronze. See methodology for full provenance.
Related comparisons
Other head-to-heads using the same scoring system:
Grok vs Fusion Hy3 vs Fusion Grok vs Hermes MoA Hy3 vs Hermes MoA Grok vs Claude Fable 5 Hy3 vs Claude Fable 5 Grok vs MiniMax M3 Hy3 vs MiniMax M3Full model pages: Grok · Hy3 · back to the leaderboard
Run this stack yourself.
Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 3,600+ founders shipping with it every day all live inside the AI Profit Boardroom.





























