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

DeepSeek V4 Flash vs DeepSeek V4 Pro

DeepSeek's cheap tier, retrained for agents — same size, sharper loops. vs DeepSeek's flagship tier — benched head-to-head against its own cheap Flash.

DeepSeek V4 Flash · context1M tokens
DeepSeek V4 Pro · context1M tokens
DeepSeek V4 Flash · priceAPI · cheap tier
DeepSeek V4 Pro · priceAPI · pro tier
DeepSeek V4 Flash · vendorDeepSeek
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 DeepSeek V4 Flash and DeepSeek V4 Pro, side by side, on 50 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.

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.

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 ↓
DeepSeek V4 Flash
DeepSeek V4 Pro
Game
DeepSeek V4 Flash on Arcade
DeepSeek V4 Pro on Arcade
Game
DeepSeek V4 Flash on Crypt
DeepSeek V4 Pro on Crypt
Game
DeepSeek V4 Flash on Dogfight
DeepSeek V4 Pro on Dogfight
Game
DeepSeek V4 Flash on Doom
DeepSeek V4 Pro on Doom
DeepSeek V4 Flash on Dragonflight
DeepSeek V4 Pro on Dragonflight
DeepSeek V4 Flash on Dragonrealm
DeepSeek V4 Pro on Dragonrealm
Game
DeepSeek V4 Flash on Flightsim
DeepSeek V4 Pro on Flightsim
Game
DeepSeek V4 Flash on Game
DeepSeek V4 Pro on Game
Game
DeepSeek V4 Flash on Gtadrive
DeepSeek V4 Pro on Gtadrive
Game
DeepSeek V4 Flash on Gtafoot
DeepSeek V4 Pro on Gtafoot
DeepSeek V4 Flash on Neonblaster
DeepSeek V4 Pro on Neonblaster
Game
DeepSeek V4 Flash on Neoncity
DeepSeek V4 Pro on Neoncity
Game
DeepSeek V4 Flash on Neonracer
DeepSeek V4 Pro on Neonracer
DeepSeek V4 Flash on Nordiccrypt
DeepSeek V4 Pro on Nordiccrypt
Game
DeepSeek V4 Flash on Outrun
DeepSeek V4 Pro on Outrun
Game
DeepSeek V4 Flash on Parachute
DeepSeek V4 Pro on Parachute
Game
DeepSeek V4 Flash on Pool
DeepSeek V4 Pro on Pool
Game
DeepSeek V4 Flash on Racing
DeepSeek V4 Pro on Racing
Game
DeepSeek V4 Flash on Raycaster
DeepSeek V4 Pro on Raycaster
Game
DeepSeek V4 Flash on Rpg
DeepSeek V4 Pro on Rpg
Game
DeepSeek V4 Flash on Skyrim
DeepSeek V4 Pro on Skyrim
DeepSeek V4 Flash on Twilightvale
DeepSeek V4 Pro on Twilightvale
Game
DeepSeek V4 Flash on Voxelcraft
DeepSeek V4 Pro on Voxelcraft
Other
DeepSeek V4 Flash on Matrixrain
DeepSeek V4 Pro on Matrixrain

Strengths & weaknesses I logged

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

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 DeepSeek V4 Flash DeepSeek V4 Pro
VendorDeepSeekDeepSeek
Context window1,000,000-token context window1,000,000-token context window
PriceAPI · cheap tierAPI · pro tier
Pricing detailBenched 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.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.
Release2026-072026-07
Bench coverage0/50 scored · avg —0/50 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 DeepSeek V4 Flash and DeepSeek V4 Pro both into the Agent Operating System and dispatch each from the kanban by task type — long agent loops and codex-style write-run-fix work, which is what the 0731 upgrade targets → DeepSeek V4 Flash, 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 — DeepSeek V4 Flash vs DeepSeek V4 Pro

Which is better, DeepSeek V4 Flash or DeepSeek V4 Pro?

On Goldie Bench, DeepSeek V4 Flash 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 DeepSeek V4 Flash cost vs DeepSeek V4 Pro?

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. 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 DeepSeek V4 Flash vs DeepSeek V4 Pro?

DeepSeek V4 Flash has a 1,000,000-token context window context window. DeepSeek V4 Pro has a 1,000,000-token context window context window.

When should I pick DeepSeek V4 Flash over DeepSeek V4 Pro?

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.

When should I pick DeepSeek V4 Pro over DeepSeek V4 Flash?

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 DeepSeek V4 Flash 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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