The cost angle is what most coverage misses. We're using Claude Haiku in
production for a small consumer app and the per-call cost is genuinely
fine, but the second you have any kind of multilingual fan-out the bill
grows non-linearly because the same query gets re-issued in N localized
contexts.
Open-weight models with strong multilingual support change the math
because you can self-host at marginal cost once you have GPU capacity.
DeepSeek's earlier versions already punched above their weight on
non-English benchmarks (especially CJK and some Indic languages where
the gap to GPT-4 was much narrower than English-only benchmarks
suggested).
Two questions for anyone who's actually deployed V4 in production yet:
1. How does it handle Turkish / Slavic morphology compared to V3? In our
tests V3 was solid for Russian and respectable for Turkish, but
handled compound morphology in agglutinative languages a bit
awkwardly.
2. Is the long-context window actually usable end-to-end or does quality
degrade past ~64k like with most open models?
This is kinda FUD. Claude really isn’t that good compared to Codex firstly and if you combine the latest DeepSeek model with any good coding harness the results are surprisingly comparable to Claude Code.
I would say DeepSeek is definitely behind compared to Codex but Claude doesn’t and hasn’t impressed me for some time now. It writes way too much code when it doesn’t need to in a fashion that gradually rots your codebase.
Codex is the only model I’ve used which will regularly remove more code than it adds or make a fix or feature by adding a single line of code or otherwise do minimal working changes.
Claude is the model which can get the feature working by adding two new classes, 20 new methods and 2000 lines of code, when it actually needed to remove 500 lines of code and add two new methods.
Claude will also often refactor by adding tons of new code and using it while not deleting any of the old code.
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[ 4.8 ms ] story [ 32.4 ms ] threadOpen-weight models with strong multilingual support change the math because you can self-host at marginal cost once you have GPU capacity. DeepSeek's earlier versions already punched above their weight on non-English benchmarks (especially CJK and some Indic languages where the gap to GPT-4 was much narrower than English-only benchmarks suggested).
Two questions for anyone who's actually deployed V4 in production yet:
1. How does it handle Turkish / Slavic morphology compared to V3? In our tests V3 was solid for Russian and respectable for Turkish, but handled compound morphology in agglutinative languages a bit awkwardly.
2. Is the long-context window actually usable end-to-end or does quality degrade past ~64k like with most open models?
Having said that I really hope this model of deepseek, performs significantly on par with the claude saunnet model.
I would say DeepSeek is definitely behind compared to Codex but Claude doesn’t and hasn’t impressed me for some time now. It writes way too much code when it doesn’t need to in a fashion that gradually rots your codebase.
Codex is the only model I’ve used which will regularly remove more code than it adds or make a fix or feature by adding a single line of code or otherwise do minimal working changes.
Claude is the model which can get the feature working by adding two new classes, 20 new methods and 2000 lines of code, when it actually needed to remove 500 lines of code and add two new methods.
Claude will also often refactor by adding tons of new code and using it while not deleting any of the old code.