Is GPT-4.1 Mini a good deal?
Whether anything in this catalogue beats GPT-4.1 Mini on both quality and price, and what you give up if it does. A computation on the current catalogue, not an opinion.
12 undominated of 145 · Oct 6, 2026
As of Oct 6, 2026, GPT-4.1 Mini is dominated for Balanced on LMArena. GPT-5.6 Luna scores 90.6 higher and costs 36% less, with a covering envelope. 12 of 145 rated, priced standard models are undominated.
Inspect model evidence Compare differences & requirements
GPT-5.6 Luna is both better and cheaper than GPT-4.1 Mini: 90.6 points higher on LMArena and 36% less per million tokens, $0.250 cheaper at this mix.
LMArena Elo · higher is better
Scale starts at 1040 Elo
1340.4
Effective $/M · Balanced · lower is better
$0.700/M
LMArena Elo · higher is better
Scale starts at 1040 Elo
1431.0
Effective $/M · Balanced · lower is better
$0.450/M
GPT-4.1 Mini takes image, text, file, returns up to 32,768 tokens from a 1,047,576-token context, and is listed by 3 sellers.
Compared against 145 rated, priced models on this lens: 3 models dominate it and give up nothing, 5 more dominate it but give something up. GPT-4.1 Mini scores 1340.4 at $0.700 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 |
|---|---|---|---|
| GPT-5.6 Luna | 1431.0 +90.6 | $0.450/M | 36% |
| Gemini 3.1 Flash Lite Preview | 1415.7 +75.3 | $0.563/M | 20% |
| GPT-6 Luna | 1391.5 +51.1 | $0.200/M | 71% |
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 |
|---|---|---|---|
| MiMo-V2.6-Pro | 1491.0 | $0.540/M | file |
| GLM 5.3 Flash | 1469.6 | $0.238/M | file |
| MiMo-V2.5-Pro | 1465.0 | $0.544/M | image, file |
| DeepSeek V4.1 Flash | 1462.5 | $0.182/M | file |
| MiMo-V2.6-Flash | 1456.4 | $0.175/M | file |
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Saved decision references
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Constraint: replacements must preserve the model’s capabilities; any losses remain named trade-offs.
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