On July 31st, DeepSeek made a move that caught the entire industry's attention: they open-sourced the official version of V4 Flash (under the MIT license). This model, activating only 13B parameters(the part of the model actually used each time), scored 50 points on a third-party intelligence index—only 1 point shy of OpenAI's closed-source flagship, GPT-5.6 Luna.
No Skeleton Change, Just Training Update, Flash Outperforms Its Own Pro
DeepSeek officially stated: the model structure and size of V4 Flash 0731 are exactly the same as the preview version from three months ago—total parameters 284B, activated 13B, 1M context window(how much text it can hold at once). The only difference is a new round of post-training(keeping the model skeleton unchanged, only adjusting the "how to answer questions" strategy, similar to giving the same person a new work method).
So what happened? According to the official announcement of 9 Agent benchmark tests, the official version of Flash completely surpassed the higher-positioned V4-Pro preview version. The most astonishing was DeepSWE (high-difficulty coding): jumping from the preview version's 7.3 to 54.4, an increase of more than 6 times. Terminal Bench 2.1 (terminal operation) scored 82.7, surpassing Pro's 72.1, and even outperforming ZhiPu GLM-5.2's 81.0, just 2.3 points shy of Claude Opus 4.8's 85.0.
In other words: the industry's default "Pro is always stronger than Flash" hierarchical logic has been broken by DeepSeek itself. The lightweight version is not a low-configuration version—it just hasn't been trained well enough.
1 Point Away from Closed-Source Ceiling, 60% Cheaper
The third-party evaluation agency Artificial Analysis's intelligence index(a comprehensive ability score across multiple tasks, with a comparable model median of about 17 points) gave V4 Flash 0731 a 50 points. What does this number mean?
Horizontally: on par with Google Gemini 3.6 Flash (50 points), 1 point shy of GPT-5.6 Luna (51 points), 7 points away from the current open-source leader Kimi K3 (57 points). Vertically: compared to its own Flash preview version, it improved by 10 points (40→50), and compared to the higher-positioned V4-Pro preview version, it is 6 points higher (44→50). This is the first time in open-source model(models whose files are public for anyone to download and build on) history to come so close to this level of closed-source flagship.
Price-wise: cache hit input 0.2 yuan/million tokens(the units AI bill by), cache miss input 1 yuan, output 2 yuan. Calculated by task cost, it is about 60% cheaper than the reduced-price GPT-5.6 Luna. OpenAI reduced Luna's price by 80% on the same day, but it is still more expensive than DeepSeek.
| Model | AA Intelligence Index | Activated Parameters | Open Source | Per Task Cost |
|---|---|---|---|---|
| V4 Flash 0731 | 50 | 13B | MIT | Baseline |
| GPT-5.6 Luna (max) | 51 | Not disclosed | No | +60% |
| Gemini 3.6 Flash | 50 | Not disclosed | No | — |
| Kimi K3 (max) | 57 | Not disclosed | Yes | — |
| V4 Pro preview version | 44 | 49B | No | Higher |
Data source: Artificial Analysis Intelligence Index (2026-07-31), DeepSeek official pricing page. Per task cost is calculated according to the Artificial Analysis standard.
Post-Training More Effective Than Parameter Stacking—Who's It Most Beneficial For?
The most counterintuitive aspect of this release: the model skeleton remains completely unchanged. 284B total parameters, 13B activated, CSA+HCA hybrid attention, 32T token training data—all the same as three months ago. The only change is the post-training strategy.
What does this mean? The current performance bottleneck of large models is likely not in "how big the model is," but in "how refined the training method is." The same skeleton, with a different post-training strategy, can achieve a qualitative leap—this is a huge boon for teams with limited computing power(the computing resources needed to run models): you don't need more GPUs, you need better training methods.
DeepSeek's official statement also hinted at a signal: the official version of V4-Pro will be launched in early August. If Pro also undergoes the same post-training upgrade, the ceiling of the open-source flagship will be raised even further.
The Balance Between Open Source and Closed Source Is Shifting
Taking a broader view. What happened in the first half of 2026? Kimi K3 was so popular it was sold out, DeepSeek V4 Flash caught up with closed source, GLM-5.2 followed closely behind—Chinese open-source models are collectively approaching the global ceiling.
Meanwhile, what is the closed-source camp doing? OpenAI reduced the price of GPT-5.6 Luna by 80% on the same day. This is not a coincidence—when open-source models are only 1 point behind on the intelligence index and 60% cheaper, the only moat left for closed-source vendors is the convenience of "you don't have to deploy it yourself."
But the MIT license means: any team can download the weights, privatize the deployment, and fine-tune(retraining it on your own materials so it fits you) according to their own needs. For data-sensitive enterprises (finance, medical, government), this is not a question of "whether to save money," but "whether it can be used." Open-source models have for the first time simultaneously met the two conditions of "smart enough" and "able to be privatized."
Available Today: Who Should Switch and How
V4 Flash 0731 has been launched on DeepSeek's first-party API, and Ollama has also listed it simultaneously (ollama run deepseek-v4-flash:0731-cloud). It natively supports the OpenAI Completion API format and is adapted for Codex.
If you have a large number of repetitive Agent tasks (code generation, terminal operation, tool invocation): prioritize testing. Flash 0731 has already approached Opus 4.8 in these tasks, with a cost 60% lower.
If you need private deployment (data does not leave the domain): you can download it now. MIT license + 167GB weights + Q8 quantization can run with 110GB memory.
If you need top-level planning/creative ability: wait a bit longer. V4-Pro official version will be launched in early August, with a higher positioning. Flash excels in "doing work," not "giving ideas."
When a 13B activated open-source model is only 1 point away from the closed-source ceiling and 60% cheaper, the strategy of "waiting for closed-source price reduction" is outdated. The first thing you can do today: run an A/B test on the most repetitive part of your Agent workflow with V4 Flash. The data will make the decision for you.
Benchmark data comes from DeepSeek's official release and third-party evaluations by Artificial Analysis. Some benchmarks (DSBench series) use DeepSeek's self-developed Harness, which is not the same set of questions as the old version of Terminal Bench 2.0, and the actual production environment performance still needs third-party reproduction.