Is DeepSeek V4 Pro 0423 a good deal?
Whether anything in this catalogue beats DeepSeek V4 Pro 0423 on both quality and price, and what you give up if it does. A computation on the current catalogue, not an opinion.
13 undominated of 136 · Sep 23, 2026
As of Sep 23, 2026, DeepSeek V4 Pro 0423 is dominated for Balanced on LMArena. GLM 5.3 Flash scores 27.9 higher and costs 80% less, with a covering envelope. 13 of 136 rated, priced standard models are undominated.
Inspect model evidence Compare differences & requirements
GLM 5.3 Flash is both better and cheaper than DeepSeek V4 Pro 0423: 27.9 points higher on LMArena and 80% less per million tokens, $0.95 cheaper at this mix.
LMArena Elo · higher is better
Scale starts at 1040 Elo
1444.0
Effective $/M · Balanced · lower is better
$1.18/M
LMArena Elo · higher is better
Scale starts at 1040 Elo
1471.9
Effective $/M · Balanced · lower is better
$0.237/M
DeepSeek V4 Pro 0423 takes text, returns up to 384,000 tokens from a 1,048,576-token context, and is listed by 16 sellers. Open weights, so it can also be self-hosted.
Compared against 136 rated, priced models on this lens: 1 model dominates it and gives up nothing, 5 more dominate it but give something up. DeepSeek V4 Pro 0423 scores 1444 at $1.18 per million tokens for this mix.
Envelope-safe replacements
Each row scores at least as high, costs no more, and covers this model’s context, output, modalities, tools, and reasoning. A cheaper narrower model is not listed here.
| Model | LMArena | Effective $/M | You save |
|---|---|---|---|
| GLM 5.3 Flash | 1471.9 +27.9 | $0.237/M | 80% |
Higher score, lower price, named losses
Not a drop-in. The loss is why these are not a recommendation.
| Model | LMArena | Effective $/M | You give up |
|---|---|---|---|
| GLM 5.2 | 1466.9 | $0.998/M | max output |
| MiMo-V2.5-Pro | 1464.8 | $0.544/M | max output |
| Qwen3.7 Plus | 1454.2 | $0.560/M | context, max output |
| GLM 5 | 1446.3 | $0.930/M | context, max output |
| Kimi K2.5 | 1445.6 | $0.900/M | context, max output |
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Constraint: replacements must preserve the model’s capabilities; any losses remain named trade-offs.
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