Kimi K3 vs GLM-5.2 vs DeepSeek V4: The Real 2026 Cost Comparison

Three models, one shortlist

Morph's coding-model comparison names exactly these three families, plus Qwen3, as the models worth evaluating for coding work in 2026, without declaring a single winner across the board [1]. Kingy.ai's shortlist goes further and reports actual standings: as of mid-2026 it puts GLM-5.2 at the top of the open-model field on the Artificial Analysis Intelligence Index and on SWE-bench Pro, a coding-specific benchmark [2]. Wavect's comparison piece frames the same families, alongside Qwen and Llama, as the current open-weight shortlist worth putting in front of a procurement review [3].

None of that is a claim this site is making about model quality — we have not run a benchmark on any of these models. It is what three separate outside reviewers say, attributed to them, so the capability half of this comparison rests on sources that can be checked rather than on our own impression.

What we can actually measure: price

The capability question above is a matter of reading someone else's benchmark. The price question is not — it is read from the same rate table this platform bills from, for each model against the rate its own publisher charges for it.

Output rate, the other half of the bill

Input tells only part of the story on a workload that writes as much as it reads. The same three models, same publisher-vs-ours comparison, on output.

The bill at a realistic coding-agent volume

A per-token rate is not a bill. The volume below is a stated assumption, not a measurement: 300 million input tokens and 15 million output tokens in a month, a 20-to-1 ratio of input to output that fits a coding agent resending a large repo context and conversation history on every turn while producing comparatively little new text back — mostly diffs and short tool calls rather than long explanations.

This figure prices every token fresh, with no repeated-prefix caching applied — a deployment that actually caches its system prompt and repo context, as a real coding agent typically does, would land lower than every bar below, on all three models.

Where capability, not price, decides

Kingy.ai's mid-2026 standings put GLM-5.2 at the top of the open-model field on the Artificial Analysis Intelligence Index and on SWE-bench Pro [2] — a real, checkable claim, but one made by that publisher, not measured by us. It says nothing about whether GLM-5.2 is the right choice for a specific codebase, a specific tool-use pattern, or a context length longer than the benchmark tested. Morph's and Wavect's pieces are more cautious, framing all three as contenders rather than picking a single leader [1][3].

The honest way to use this article is the reverse of how a vendor page usually wants it read: take the price numbers above as fact, because they are read from the same table this platform bills from, and take the capability claims as one outside publisher's measurement, worth weighing but not worth treating as settled.