GPT-5.6 vs DeepSeek V4: Closed vs Open Weights
GPT-5.6 vs DeepSeek V4: OpenAI’s $5/$30 flagship against an open-weight model at ~$0.14/$0.28. What a ~35x price gap buys on one-shot coding, judged live.
OpenAI’s GPT-5.6 Sol is the frontier tier of the new 5.6 family: $5 per million input tokens, $30 per million output. DeepSeek V4 Flash is an open-weight model whose FP8 endpoint on OpenRouter runs about $0.14 and $0.28. That is roughly a 35x gap on input pricing — and over 100x on output — the widest closed-versus-open spread in our roster. We covered the Sol/Terra/Luna launch in GPT-5.6 is here; this piece asks the narrower question the price sheet begs: on a one-shot coding prompt, what does the premium actually buy?
| GPT-5.6 Sol | DeepSeek V4 Flash | |
|---|---|---|
| Price (per 1M in / out) | $5 / $30 | ~$0.14 / ~$0.28 (OpenRouter) |
| Weights | Closed — API access only | Open — download, self-host, fine-tune |
| Quantization | Undisclosed; served only by OpenAI | FP8 — we benchmark the OpenRouter FP8 endpoint |
| Role in our roster | Frontier flagship (low + max effort) | Open-weight value pick (default effort) |
The 35x math, per generation
Per-token ratios are abstract; per-task costs are not. A one-shot arena build — a landing page, a small game, a dashboard — typically lands in the low tens of thousands of output tokens. At Sol’s $30 per million, a 15,000-token generation costs about 45 cents. The same generation at V4 Flash prices costs about four tenths of a cent. Call it a hundred V4 Flash runs for one Sol run. If code generation is something you do all day, that ratio is not a rounding error; it is the budget.
What you give up going open-weight — if anything
The honest list is shorter than the price gap implies. With Sol you get OpenAI’s own serving stack, effort-level controls, and whatever the flagship carries that never shows up on a spec sheet. With V4 Flash you get the model itself: weights you can download, host anywhere, fine-tune, and keep using after any single provider deprecates an endpoint. What you give up is certainty about the serving configuration — which is exactly why we pin ours down.
An open-weight result is always a result for one serving configuration. Our DeepSeek V4 Flash column is the OpenRouter FP8 endpoint — the same hosted, quantized deployment you would actually pay for. A different host at a different precision can behave differently. Sol carries no such ambiguity: OpenAI serves it, and only OpenAI.
How we compare them: one shot, blind, live
Both models get the same prompt, one attempt, no retries, no cherry-picking. The outputs render live in the browser — real running artifacts, not screenshots — and the community votes without knowing which model produced which build. You can watch a live GPT-5.6 Sol vs DeepSeek V4 head-to-head on the landing-page task right now. We are deliberately not quoting vote tallies in this post, because tallies move; the leaderboard always has the current community-voted standings for both, next to every other family in the arena.
When to pick which
- Pick Sol when a single generation carries real stakes — the task is hard, the retry loop is expensive, and a 45-cent build that lands first time beats five cheap ones that don’t.
- Pick V4 Flash when volume dominates — scaffolding, iteration, throwaway prototypes, anything where you would trade a marginal quality delta for two orders of magnitude on price.
- Pick neither on faith. The premium is only justified where Sol’s output visibly wins, and that is an empirical question the arena answers per prompt, not a property of the logo.
The 35x gap is the whole story only if the outputs are equal — and whether they are is checkable, not debatable. Open the arena, put both on the same prompt, judge blind, and vote. And if price-versus-produced-value is the part that hooks you, what tokens actually buy is the longer argument for judging model spend by output instead of by invoice.
Frequently asked questions
Is DeepSeek V4 as good as GPT-5.6 Sol for coding?
There is no static answer worth trusting. Both run the same one-shot coding prompts in our arena, outputs render live, and the community votes blind. Price differs by roughly 35x; whether quality differs at all is what the votes decide, and standings move as votes come in — check the live leaderboard rather than a snapshot.
How much cheaper is DeepSeek V4 than GPT-5.6?
GPT-5.6 Sol costs $5 per million input tokens and $30 per million output. The OpenRouter FP8 endpoint for DeepSeek V4 Flash runs about $0.14 and $0.28. That is roughly 35x on input and over 100x on output — about a hundred V4 Flash generations for the price of one Sol run on an output-heavy coding task.
Does FP8 quantization hurt DeepSeek V4 quality?
FP8 is the precision the model is commonly served at, and it is exactly what we benchmark: the OpenRouter FP8 endpoint. Results reflect that serving configuration, not the weights in the abstract — a different host at a different precision could behave differently. The arena shows what the endpoint you can actually buy produces.
Can I run DeepSeek V4 locally?
In principle yes — the weights are open, which is the point. In practice it is a large model, and most people use hosted endpoints. The practical win of open weights is choice of host, price competition, and no deprecation risk, not necessarily running it on a home GPU.
Don’t take the post’s word for it
The arena runs every model’s real output live. Pick a challenge, go blind, and cast a vote that counts in the public tally.
Open the arena