DeepSeek V4-Pro Beats Opus-4.8 on DeepSWE, Then Prices Jump the Same Week
Quick verdict
DeepSeek released V4-Pro-0813 with open weights and a big coding jump, DeepSWE went from 12.8 on the preview to 62.7, which early testers put ahead of GLM-5.2 and Claude Opus-4.8 on that one benchmark. It does this at 1.7T parameters, smaller than Kimi K3 at 2.8T. Then, the same week, DeepSeek raised API prices, with cache hits on V4-Pro climbing as much as 1,114% at peak hours starting August 16. So the model got better and the cheapest reason to use it got weaker at the same time. If you run DeepSeek through the API for the low price, read the pricing section before you route more traffic to it.
What actually shipped
DeepSeek announced V4-Pro-0813 and published the weights on Hugging Face at deepseek-ai/DeepSeek-V4-Pro-0813, so you can host it yourself or wait for third-party inference providers to pick it up. The headline number is the coding jump. On DeepSWE, the model moved from 12.8 on the V4-Pro Preview to 62.7, a swing that testers flagged as ahead of GLM-5.2 and Opus-4.8 on that benchmark. It reportedly does this at 1.7T parameters against Kimi K3's 2.8T, which is the part that got infra people talking: a smaller model landing near the top on a hard coding eval.
| Detail | DeepSeek V4-Pro-0813 |
|---|---|
| DeepSWE (reported) | 62.7 (up from 12.8 on preview) |
| Parameters | ~1.7T |
| Weights | Open, on Hugging Face |
| Compared against | Ahead of GLM-5.2, Opus-4.8 on DeepSWE (per testers) |
| Rollout | Hosted API plus self-host |
The launch was not clean. The Hugging Face repo briefly returned a 404 and went private not long after it went up. The likely reason, per commenters who looked at the files: the config.json declared 43 hidden layers, matching the smaller Flash variant, while the downloaded weight shards contained 61 layers. That mismatch reads like a packaging error, and the repo appears to have been pulled and corrected before reappearing. So treat the very first benchmark numbers with a little caution, they came off an artifact that DeepSeek itself seems to have re-cut.
One early tester also pushed back on the parity claims. In their hands, V4-Pro did not match Kimi K3's knowledge depth or its ability to sustain long, hands-off project work across a big context. That is the usual gap between a benchmark score and a full day of agent work, and it is worth remembering before you swap your main model on the strength of one DeepSWE figure.
The price hike changes the math
The catch landed the same week. DeepSeek is changing its API pricing effective August 16, 2026 at 16:00 UTC, and it is not a small move. The new structure adds peak and off-peak windows, where peak pricing is 2x off-peak. The sharpest increases hit cached input, which is exactly what long-context and repetitive agent workloads lean on.
| V4-Pro pricing (per unit) | Before | After (off-peak / peak) |
|---|---|---|
| Cache hit (input) | $0.003625 | $0.022 / $0.044 (+507% / +1,114%) |
| Output | $0.87 | $1.98 / $3.96 (+128% / +355%) |
Cache hits were DeepSeek's quiet advantage. If you ran an agent that re-sent the same long system prompt and context on every turn, cached input made that cheap. A jump to as much as 1,114% at peak wipes out most of that edge for the workloads that depended on it. Output going up 128% to 355% stacks on top. The community reaction on the launch and pricing threads was blunt: several people said they had already shifted away, on the logic that V4 was worth it mainly because it was cheap, and it is now less cheap.
Why it matters
DeepSeek spent two years as the default answer to "what is the good model that costs almost nothing." That reputation carried real weight, people tolerated it being token-hungry and a little slow because the bill was tiny. V4-Pro is the best model the company has shipped, and the price move lands in the same week. The signal is that DeepSeek wants to be judged on capability now, not just on being the budget pick.
For anyone routing production traffic, that is a real decision. If you were on DeepSeek purely for cost, the case for staying weakens on August 16, and two doors open. One is self-hosting, since the weights are public, which only pays off at real volume. The other is looking harder at the other cheap-and-good open models, where Chinese open models keep trading the top of the OpenRouter charts and Gemini 3.7 Flash just cut its own price. The point is that "DeepSeek because it is cheapest" is no longer an automatic answer.
Video: DeepSeek V4-Pro-0813, tested
A hands-on run through V4-Pro-0813 against the earlier V4-Flash, useful for seeing how the coding jump holds up outside the benchmark table.
How to think about the choice
If your DeepSeek usage is mostly agentic coding with long cached context, price out your August bill under the new rates before you do anything, because the cache-hit change is where it will bite. If the number jumps, the cleanest fix is routing: keep the hard planning on your main model and send cheap high-volume work to whatever wins on price that week. Our guide to cutting coding-agent costs with model routing covers the split, and routing across open models gets into doing it at scale. For where V4-Pro sits among the current options, see the best AI models for coding in 2026.
FAQ
Is DeepSeek V4-Pro actually better than Opus-4.8?
On the DeepSWE benchmark, early testers put V4-Pro-0813 ahead of both GLM-5.2 and Opus-4.8, with a score of 62.7. One benchmark is not the whole story, though, and at least one tester said it still trails Kimi K3 on knowledge depth and long hands-off work. Test it on your own tasks before you switch.
Are the weights really open?
Yes. DeepSeek published V4-Pro-0813 on Hugging Face, so you can self-host it. The repo briefly went private right after launch over a config mismatch, a 43-layer config.json against 61-layer weight shards, and appears to have been re-cut and restored.
How much are DeepSeek's API prices going up?
Starting August 16, 2026, V4-Pro cache hits rise from $0.003625 to $0.022 off-peak and $0.044 at peak, up to a 1,114% increase. Output moves from $0.87 to $1.98 off-peak and $3.96 at peak. Peak pricing is 2x off-peak, so when you run matters now. See our breakdown of the DeepSeek price hike for what to do about it.
Should I switch away from DeepSeek?
If you were on it purely for the low price, run the new numbers first. Self-hosting the open weights can make sense at high volume, and other cheap open models are competitive. If DeepSeek's quality is what you need, the higher price may still beat the alternatives for your workload.
Sources
- @deepseek_ai - launching DeepSeek-V4-Pro
- r/LocalLLaMA - DeepSeek-V4-Pro launch thread and pricing reaction
- r/LocalLLaMA - HF benchmarks, 1.7T scale, and the config/weights mismatch
- r/DeepSeek - API price increase effective August 16
- DeepSeek API pricing docs
Further reading
Try all the models mentioned in this article
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