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I’ve been using DeepSeek V4 in OpenCode exclusively for about a month.

I think it’s great, but coming from Claude Code it did feel like going back in time by ~6 months in model capabilities. This isn’t a big deal to me for what I do, but the difference is definitely there.

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Deepseek v4 Pro is like Opus 4.5 or GPT 5.2, but costs pennies on the pound for API. Which is to say, I should definitely be using it more to let my Codex and Claude subs go further.
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Opus 4.5 was definitely stronger than DeepSeek V4 for me, specifically with large context.

I’m being pedantic/splitting hairs, though. I’ve obviously switched to DeepSeek full-time because it makes more sense to me pragmatically — I spend a few more tokens to get the outcome I want, but the tokens are cheap as dirt and the API is faster.

Perhaps I should plug it into Claude Code and see how it performs? I haven’t tried that.

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Opus 4.8 and GPT 5.5 are the best models, but people don't care about "best" anymore, until there is a big leap in capability I don't think anyone will care about point releases.

Vibes and tribalism will prevail until one of emerges as clearly and unambiguously superior to the other.

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I get what you mean but the GPU comparison isn't the best here, I think. Money-is-no-object-I-want-the-best approach is questionable, definitely. But no one can argue that an old Nvidia card is objectively better for e.g. 4k gaming than a 4090 if you don't mind the wattage. You can just measure it.

With LLMs the problem is more complex, it's people getting used to how a model works and to the ecosystem. Sure, you can make all your skills harness-agnostic and deal with Anthropic's stubborn refusal to adopt the common naming/directory structure. But most people don't. So then you end up with something closer to the ancient Android vs iOS discussion. Can you prove, in isolation, that iOS is more energy efficient, the hardware is faster? Yeah. But that won't speak to someone who has been on Android for 10 years and would have to migrate and get used to iOS to experience that, first.

I've noticed myself how I get used to common failure modes of particular models in my projects. GPT5.5 tends to create some checks/booleans I don't need, it heavily overcorrects on error handling, etc. While Claude 4.7/4.8 doesn't do those as often but gets derailed on our E2E test suite, forgets to run linting despite guidance. So even assuming fully harness-agnostic working setup, a new LLM model with its own quirks can be a lot friction for heavy users who might be used to Claude specifically and all their skills/guidance pre-address common failure modes.

E.g. I might be a Prius owner, then you gift me an objectively better, more efficient, safer, newer, same-size, physical knobs car ...and I might still swear by my Prius! I'm used to how it turns, how it feels, I can repair some issues myself. Isn't that a normal reaction then?

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> Same reason people buy the RTX 4090 and 5090 cards - overpriced but they must have the "best".

Or they need to run high VRAM apps like LLMs

Or they have 4K monitors and want smooth gameplay on them

Is this whole thread just dedicated to snark about other people’s personal preferences?

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Hey, at least the superior performance of a 4090 or a 5090 can be objectively measured.
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But it's a matter of degrees better, not miles.
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You're projecting
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