Get the Agent OS + join 4,000+ founders inside the AI Profit Boardroom → Join AIPB ($59/mo)
Real head-to-head · same prompt, one shot

Qwen 3.8 vs DeepSeek V4 Flash

Alibaba's 2.4T flagship — benched through Qoder. vs DeepSeek's cheap tier, retrained for agents — same size, sharper loops.

Qwen 3.8 · contextQoder-hosted
DeepSeek V4 Flash · context1M tokens
Qwen 3.8 · priceQoder plan
DeepSeek V4 Flash · priceAPI · cheap tier
Qwen 3.8 · vendorAlibaba
DeepSeek V4 Flash · 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 Qwen 3.8 and DeepSeek V4 Flash, side by side, on 45 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.

Qwen 3.8 · Benched on GoldieBench via the Qoder CLI (`qoder-qwen`, model Qwen3.8-Max-Preview) — the only door while it has no public API. Non-game tasks are one-shot like the rest of the field. GAME tasks run in Qoder's real AGENT mode: skill-infused build, then up to 2 QA fix rounds where a vision judge + live console errors are fed back and Qwen 3.8 edits its own file (it took crypt from a black-screen 3.0 to a torch-lit 7.8). Scored on a mid-play frame by the same Opus judge as everyone else.

DeepSeek V4 Flash · Wired into the Agent OS three ways: a `deepseek` Hermes profile, the DeepSeek Coder tab (official API, V4 Flash 0731 / V4 Pro picker, live preview), and the OpenCode model dropdown. Benched on all 50 GoldieBench tasks via api.deepseek.com — the endpoint the 0731 beta shipped on — with the skill-infused threejs-game-director prompt on game tasks and a model-driven fix round on any build that failed the render check.

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 ↓
Qwen 3.8
DeepSeek V4 Flash
Game
Qwen 3.8 on Arcade
DeepSeek V4 Flash on Arcade
Game
Qwen 3.8 on Crypt
DeepSeek V4 Flash on Crypt
Game
Qwen 3.8 on Dogfight
DeepSeek V4 Flash on Dogfight
Game
Qwen 3.8 on Doom
DeepSeek V4 Flash on Doom
Qwen 3.8 on Dragonflight
DeepSeek V4 Flash on Dragonflight
Qwen 3.8 on Dragonrealm
DeepSeek V4 Flash on Dragonrealm
Game
🥉Qwen 3.8 on Flightsim
DeepSeek V4 Flash on Flightsim
Game
Qwen 3.8 on Game
DeepSeek V4 Flash on Game
Game
🥈Qwen 3.8 on Gtadrive
DeepSeek V4 Flash on Gtadrive
Game
Qwen 3.8 on Gtafoot
DeepSeek V4 Flash on Gtafoot
🥈Qwen 3.8 on Neonblaster
DeepSeek V4 Flash on Neonblaster
Game
Qwen 3.8 on Neonracer
DeepSeek V4 Flash on Neonracer
Qwen 3.8 on Nordiccrypt
DeepSeek V4 Flash on Nordiccrypt
Game
🥇Qwen 3.8 on Outrun
DeepSeek V4 Flash on Outrun
Game
Qwen 3.8 on Racing
DeepSeek V4 Flash on Racing
Game
Qwen 3.8 on Rpg
DeepSeek V4 Flash on Rpg
Game
🥉Qwen 3.8 on Skyrim
DeepSeek V4 Flash on Skyrim
Qwen 3.8 on Twilightvale
DeepSeek V4 Flash on Twilightvale
Other
🥈Qwen 3.8 on Matrixrain
DeepSeek V4 Flash on Matrixrain
🥇Qwen 3.8 on Mlx Speedtest
DeepSeek V4 Flash on Mlx Speedtest
Other
Qwen 3.8 on Neonsnake
DeepSeek V4 Flash on Neonsnake
Page
🥇Qwen 3.8 on Aipbpromo
DeepSeek V4 Flash on Aipbpromo
Page
Qwen 3.8 on Landing
DeepSeek V4 Flash on Landing
Page
Qwen 3.8 on Webos
DeepSeek V4 Flash on Webos

Strengths & weaknesses I logged

Qwen 3.8

Strengths

  • Skyrim-style open worlds — the Dragon Realm build rendered a lit snowfield, first-person sword and working roaming enemies (7.8)
  • Held up across game genres early — voxel sandbox and Doom raycaster both came out shippable
  • Runs as a real agentic coder inside Qoder (writes + iterates on files), not just a chat model

Trade-offs

  • Torch-lit dungeon (crypt) came out generic (6.3); one open-world RPG one-shot black-screened (twilightvale 2.5) — classic three.js r128 API drift
  • Preview is Qoder / Token-Plan only — no OpenRouter or public API, so it can't be routed into an app the way the OpenRouter models can

DeepSeek V4 Flash

Strengths

  • 50/50 one-shot builds returned complete, valid, closing HTML — zero truncations
  • 42/50 rendered clean first time; all 8 dark builds were repaired by the model itself in one fix round
  • 1M-token context on the cheap tier — whole codebases fit in a single call

Trade-offs

  • Unranked — the 50 builds are on the bench but not yet scored by the Opus vision judge
  • Reasons at length before writing (a full 3D game build ran ~4-8 minutes), so it is not a fast-draft model
  • 8 of 50 first-pass builds rendered black or near-black before the fix round

Pricing & context — the spec sheet

Spec Qwen 3.8 DeepSeek V4 Flash
VendorAlibabaDeepSeek
Context windowServed through Alibaba's Qoder agent platform; the 3.8-Max preview has no standalone public context window yet.1,000,000-token context window
PriceQoder planAPI · cheap tier
Pricing detailQwen3.8-Max-Preview is Alibaba's ~2.4T-parameter flagship, positioned just behind Claude Fable 5. It is NOT on OpenRouter or a public API yet — the only access today is inside Qoder (Alibaba's agentic coding platform, free 2-week Pro trial). Benched here via the Qoder CLI on model `Qwen3.8-Max-Preview`, one-shot.Benched on the DeepSeek-V4-Flash-0731 public beta, launched 2026-07-31 on DeepSeek's official API. DeepSeek describe it as a major upgrade to agent capabilities whose benchmark scores now surpass their previous V4-Pro-Preview, using the exact same model architecture and size as the preview — the gain is post-training, not scale. Natively supports the Responses API format and is adapted for Codex-style coding loops. Benched against api.deepseek.com directly because third-party routes list "v4-flash" undated and may still serve the older preview build.
Release2026-072026-07
Bench coverage45/45 scored · avg 8.10/100/50 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 Qwen 3.8 and DeepSeek V4 Flash both into the Agent Operating System and dispatch each from the kanban by task type — one-shot 3d game and world prototypes where atmosphere matters → Qwen 3.8, long agent loops and codex-style write-run-fix work, which is what the 0731 upgrade targets → DeepSeek V4 Flash. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Qwen 3.8 vs DeepSeek V4 Flash

Which is better, Qwen 3.8 or DeepSeek V4 Flash?

On Goldie Bench, Qwen 3.8 averages no scored verdicts yet across the shared tasks, with 8 gold, 7 silver, 7 bronze overall. DeepSeek V4 Flash 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 Qwen 3.8 cost vs DeepSeek V4 Flash?

Qwen 3.8: Qwen3.8-Max-Preview is Alibaba's ~2.4T-parameter flagship, positioned just behind Claude Fable 5. It is NOT on OpenRouter or a public API yet — the only access today is inside Qoder (Alibaba's agentic coding platform, free 2-week Pro trial). Benched here via the Qoder CLI on model `Qwen3.8-Max-Preview`, one-shot. DeepSeek V4 Flash: Benched on the DeepSeek-V4-Flash-0731 public beta, launched 2026-07-31 on DeepSeek's official API. DeepSeek describe it as a major upgrade to agent capabilities whose benchmark scores now surpass their previous V4-Pro-Preview, using the exact same model architecture and size as the preview — the gain is post-training, not scale. Natively supports the Responses API format and is adapted for Codex-style coding loops. Benched against api.deepseek.com directly because third-party routes list "v4-flash" undated and may still serve the older preview build.

What's the context window for Qwen 3.8 vs DeepSeek V4 Flash?

Qwen 3.8 has a Served through Alibaba's Qoder agent platform; the 3.8-Max preview has no standalone public context window yet. context window. DeepSeek V4 Flash has a 1,000,000-token context window context window.

When should I pick Qwen 3.8 over DeepSeek V4 Flash?

Pick Qwen 3.8 for: One-shot 3D game and world prototypes where atmosphere matters; Anyone already in the Qoder IDE/CLI wanting a near-frontier model free on the Pro trial; A cheaper stand-in for Fable 5 on creative-visual builds. The trade-off is the weaknesses we logged on the bench: Torch-lit dungeon (crypt) came out generic (6.3); one open-world RPG one-shot black-screened (twilightvale 2.5) — classic three.js r128 API drift; Preview is Qoder / Token-Plan only — no OpenRouter or public API, so it can't be routed into an app the way the OpenRouter models can.

When should I pick DeepSeek V4 Flash over Qwen 3.8?

Pick DeepSeek V4 Flash for: Long agent loops and Codex-style write-run-fix work, which is what the 0731 upgrade targets; Whole-repo or whole-document tasks that need the 1M context on a cheap tier; Volume build work where you would rather wait a few minutes than pay a flagship. The trade-off is the weaknesses we logged on the bench: Unranked — the 50 builds are on the bench but not yet scored by the Opus vision judge; Reasons at length before writing (a full 3D game build ran ~4-8 minutes), so it is not a fast-draft model; 8 of 50 first-pass builds rendered black or near-black before the fix round.

How does Goldie Bench score Qwen 3.8 vs DeepSeek V4 Flash?

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.

4,000+founders
258documented wins
38countries
$59/momonthly