41–50 of 493 posts
I find it interesting how much adoption seems to be influenced by momentum. Some of these Chinese models are surprisingly capable, but developers often default to the models that are already established as the “industry standard
For funded startups it mostly just doesn’t matter a ton unless you are passing on inference in your product at scale
Benchmarks: | Benchmark | DS-V4-Pro | DS-V4-Flash | DS-V4-Pro | DS-V4-Flash | GLM-5.2 | Kimi-K3 | Opus-4.8 | Fable 5 | | | 0813 | 0731 | Preview | Preview | | | | (w/ fallback) | |--------------------------|-----------|-------------|-----------|-------------|-----------|-----------|-----------|---------------| | HLE (wo/w tools) | 42.7/60.0 | 37.8/51.5 | 37.7/48.2 | 34.8/45.1 | 40.5/54.7 | 43.5/56.0 | 49.8/57.9 | 53.…
So it's a Fable class LLM? DSV4Pro vs Fable5 HLE w tools 60.0 vs 63.0 Terminal Bench 2.1 87.9 vs 88.0 Cybergym 83.3 vs 83.1 DeepSWE 62.7 vs 70.0 Toolathlon-Verified 74.1 vs 77.9 AutomationBench (Public) 31.8 vs 29.1 DSBench-FullStack 71.1 vs 77.2 DSBench-Hard 67.2 vs 68.3
https://api-docs.deepseek.com/quick_start/pricing/ Competitive with opus 4.8 but weaker than sol or fable. About 20x cheaper.
For DeepSeek V4 Pro the typical split is 750 in, 290 out, 82k cached.
Cost per request for V4 Pro: $0.000875 per request.
Equivalent Opus cost (w/o taking into account cache write costs): $0.052 per request.
I find it interesting how much adoption seems to be influenced by momentum. Some of these Chinese models are surprisingly capable, but developers often default to the models that are already established as the “industry standard
In an enterprise setting Chinese models are often discouraged due to political risk. They don't want to need to remove a model that's deeply embedded in their stack. And it's entirely feasible that the US gov bans federal contractors from using them in the next 6 months for example, or that EU AI safety rules effectively ban them too.
Fully compatible with the well known Antrophic API.
You only have to replace the URL and your key.
Benchmarks: | Benchmark | DS-V4-Pro | DS-V4-Flash | DS-V4-Pro | DS-V4-Flash | GLM-5.2 | Kimi-K3 | Opus-4.8 | Fable 5 | | | 0813 | 0731 | Preview | Preview | | | | (w/ fallback) | |--------------------------|-----------|-------------|-----------|-------------|-----------|-----------|-----------|---------------| | HLE (wo/w tools) | 42.7/60.0 | 37.8/51.5 | 37.7/48.2 | 34.8/45.1 | 40.5/54.7 | 43.5/56.0 | 49.8/57.9 | 53.…
The timing looks like they are trying to take the wind out of Qwen's sails by releasing this on the same day that Qwen released the weights of Qwen3.8-max. Or maybe it's coincidence... For comparison I looked at Qwen's claimed benchmarks for Qwen3.8-max ( https://qwen.ai/blog?id=qwen3.8 ). Assuming each published set of benchmarks is believable, it looks like v4 Pro 0813 is better on average but overall performance i…
Given how I think DeepSeek operates... I think they just release it when they feel it's ready, and don't even seem that concerned with what other people are doing.
V4 Pro has vision correct?
Benchmarks: | Benchmark | DS-V4-Pro | DS-V4-Flash | DS-V4-Pro | DS-V4-Flash | GLM-5.2 | Kimi-K3 | Opus-4.8 | Fable 5 | | | 0813 | 0731 | Preview | Preview | | | | (w/ fallback) | |--------------------------|-----------|-------------|-----------|-------------|-----------|-----------|-----------|---------------| | HLE (wo/w tools) | 42.7/60.0 | 37.8/51.5 | 37.7/48.2 | 34.8/45.1 | 40.5/54.7 | 43.5/56.0 | 49.8/57.9 | 53.…
The timing looks like they are trying to take the wind out of Qwen's sails by releasing this on the same day that Qwen released the weights of Qwen3.8-max. Or maybe it's coincidence... For comparison I looked at Qwen's claimed benchmarks for Qwen3.8-max ( https://qwen.ai/blog?id=qwen3.8 ). Assuming each published set of benchmarks is believable, it looks like v4 Pro 0813 is better on average but overall performance i…
I can't wait to try this new one.