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Real head-to-head · same prompt, one shot

Hy3 vs DeepSeek V4 Pro

Tencent's open-weights coder — Apache-2.0, cheap, beats GLM-5.1 on frontend in Tencent's blind eval. vs DeepSeek's flagship tier — benched head-to-head against its own cheap Flash.

Hy3 · context262K tokens
DeepSeek V4 Pro · context1M tokens
Hy3 · price$0.14 / 1M input · $0.58 / 1M output
DeepSeek V4 Pro · priceAPI · pro tier
Hy3 · vendorTencent Hunyuan
DeepSeek V4 Pro · vendorDeepSeek

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 Hy3 and DeepSeek V4 Pro, 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.

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).

DeepSeek V4 Pro · Benched on all 50 GoldieBench tasks via api.deepseek.com with the same pipeline as the Flash 0731 run, then published as a live side-by-side: goldiebench.com/vs-live/deepseek-flash-vs-pro.html loads both builds of every task in twin panes.

Side-by-side on 50 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 = 🥉).

Task ↓
Hy3
DeepSeek V4 Pro
Game
Hy3 on Doom
DeepSeek V4 Pro on Doom
Hy3 on Dragonrealm
DeepSeek V4 Pro on Dragonrealm
Game
Hy3 on Flightsim
DeepSeek V4 Pro on Flightsim
Game
Hy3 on Gtadrive
DeepSeek V4 Pro on Gtadrive
Game
Hy3 on Gtafoot
DeepSeek V4 Pro on Gtafoot
Game
Hy3 on Parachute
DeepSeek V4 Pro on Parachute
Page
Hy3 on Aipbpromo
DeepSeek V4 Pro on Aipbpromo
Game
— not attempted —
DeepSeek V4 Pro on Arcade
Game
— not attempted —
DeepSeek V4 Pro on Crypt
Game
— not attempted —
DeepSeek V4 Pro on Dogfight
— not attempted —
DeepSeek V4 Pro on Dragonflight
Game
— not attempted —
DeepSeek V4 Pro on Game
— not attempted —
DeepSeek V4 Pro on Neonblaster
Game
— not attempted —
DeepSeek V4 Pro on Neoncity
Game
— not attempted —
DeepSeek V4 Pro on Neonracer
— not attempted —
DeepSeek V4 Pro on Nordiccrypt
Game
— not attempted —
DeepSeek V4 Pro on Outrun
Game
— not attempted —
DeepSeek V4 Pro on Pool
Game
— not attempted —
DeepSeek V4 Pro on Racing
Game
— not attempted —
DeepSeek V4 Pro on Raycaster
Game
— not attempted —
DeepSeek V4 Pro on Rpg
Game
— not attempted —
DeepSeek V4 Pro on Skyrim
— not attempted —
DeepSeek V4 Pro on Twilightvale
Game
— not attempted —
DeepSeek V4 Pro on Voxelcraft

Strengths & weaknesses I logged

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

DeepSeek V4 Pro

Strengths

  • Flagship reasoning tier on the same official API and 1M context as Flash
  • Ran the identical 50-prompt set as V4 Flash 0731 — a clean same-vendor A/B
  • Reasoning-first: thinks before writing every build

Trade-offs

  • Unranked — builds are on the bench but not yet scored by the Opus vision judge
  • Slower and pricier per build than Flash — the whole question is whether that buys quality

Pricing & context — the spec sheet

Spec Hy3 DeepSeek V4 Pro
VendorTencent HunyuanDeepSeek
Context window262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter.1,000,000-token context window
Price$0.14 / 1M input · $0.58 / 1M outputAPI · pro tier
Pricing detailTencent 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.DeepSeek's flagship tier, benched on `deepseek-v4-pro` via api.deepseek.com — the exact same 50 one-shot prompts, skill-infused game prompt and pipeline as the V4 Flash 0731 run, so the two runs are directly comparable side by side. DeepSeek's own line on the 0731 Flash refresh is that its post-training now beats the older V4-Pro-Preview — this run tests the current Pro against that claim.
Release2026-07-062026-07
Bench coverage7/7 scored · avg 6.76/100/50 scored · avg —

The verdict — which should you pick?

Not enough scored shared tasks yet for a head-to-head average. The live demos for both are on the matrix above — play them and form your own opinion.

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 Hy3 and DeepSeek V4 Pro both into the Agent Operating System and dispatch each from the kanban by task type — cost-sensitive coding + frontend design where open weights matter → Hy3, checking whether deepseek's pro tier is worth the premium over flash 0731 → DeepSeek V4 Pro. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Hy3 vs DeepSeek V4 Pro

Which is better, Hy3 or DeepSeek V4 Pro?

On Goldie Bench, Hy3 averages no scored verdicts yet across the shared tasks, with 0 gold, 0 silver, 0 bronze overall. DeepSeek V4 Pro averages no scored verdicts yet, with 0 gold, 0 silver, 0 bronze. Not enough scored shared tasks yet to call a winner.

How much does Hy3 cost vs DeepSeek V4 Pro?

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. DeepSeek V4 Pro: DeepSeek's flagship tier, benched on `deepseek-v4-pro` via api.deepseek.com — the exact same 50 one-shot prompts, skill-infused game prompt and pipeline as the V4 Flash 0731 run, so the two runs are directly comparable side by side. DeepSeek's own line on the 0731 Flash refresh is that its post-training now beats the older V4-Pro-Preview — this run tests the current Pro against that claim.

What's the context window for Hy3 vs DeepSeek V4 Pro?

Hy3 has a 262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter. context window. DeepSeek V4 Pro has a 1,000,000-token context window context window.

When should I pick Hy3 over DeepSeek V4 Pro?

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.

When should I pick DeepSeek V4 Pro over Hy3?

Pick DeepSeek V4 Pro for: Checking whether DeepSeek's pro tier is worth the premium over Flash 0731; Hard single-shot builds where extra reasoning depth may pay off. The trade-off is the weaknesses we logged on the bench: Unranked — builds are on the bench but not yet scored by the Opus vision judge; Slower and pricier per build than Flash — the whole question is whether that buys quality.

How does Goldie Bench score Hy3 vs DeepSeek V4 Pro?

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.

The same stack Julian uses

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 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.

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