GPT-5.2-Codex vs GLM 5
GPT-5.2-Codex is OpenAI's coding-focused model built for long, independent engineering sessions with a 400K context window and mandatory reasoning. GLM 5 is Z.ai's open-source flagship aimed at the same agentic coding niche, at roughly a third of the input price. Both are recent (GPT-5.2-Codex released 2026-01-14, GLM 5 a month later on 2026-02-11) and both show up on Design Arena's agents leaderboard, which is where this comparison gets interesting.
Spec vs spec
| Spec | GPT-5.2-Codex | GLM 5 |
|---|---|---|
| Context window | 400K | 203K |
| Max output | 128K | — |
| Input modalities | text, image | text |
| Output modalities | text | text |
| Released | Jan 14, 2026 | Feb 11, 2026 |
| Reasoning | always on | optional |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
openai/gpt-5.2-codex
Input · 1M tokens
$1.75 + 3%$1.80
Output · 1M tokens
$14.00 + 3%$14.42
Cache read · 1M tokens
$0.175 + 3%$0.180
FEE — FLAT, EVERY MODEL3%
z-ai/glm-5
Input · 1M tokens
$0.600 + 3%$0.618
Output · 1M tokens
$1.92 + 3%$1.98
Cache read · 1M tokens
$0.120 + 3%$0.124
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
openai/gpt-5.2-codex
$46.87
$45.50 provider + 3%
z-ai/glm-5Cheaper
$10.14
$9.84 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| GPT-5.2-Codex | GLM 5 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| androidnative | 1176 | #15 | 1244 | #6 |
| Full-stack | 1060 | #27 | 1190 | #13 |
| godotgamedev | 1187 | #12 | 1237 | #3 |
| Mobile apps | 1172 | #24 | 1222 | #10 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
On a 10M input / 2M output token job, GPT-5.2-Codex costs $45.50 versus GLM 5's $9.84 — a 4.6x gap. Provider list prices: GPT-5.2-Codex is $1.75/M input, $14.00/M output; GLM 5 is $0.60/M input, $1.92/M output. On OpenKey, the flat 3% fee applies to both: GPT-5.2-Codex becomes $1.8025/M input ($1.75 x 1.03) and $14.42/M output ($14.00 x 1.03); GLM 5 becomes $0.618/M input ($0.60 x 1.03) and $1.9776/M output ($1.92 x 1.03). The input price ratio alone is 2.92x. Cache reads narrow the gap slightly — $0.175/M for GPT-5.2-Codex vs $0.12/M for GLM 5 — but output tokens dominate coding workloads, and that's where GLM 5 wins hardest.
Agentic coding benchmarks
Design Arena's agents arena is the only head-to-head data here, and GLM 5 wins every category both models were scored in: androidnative (GLM 5 rank 6, elo 1244, 62% win rate vs GPT-5.2-Codex rank 15, elo 1176, 47.5%), fullstack (rank 13, elo 1190 vs rank 27, elo 1060), godotgamedev (rank 3, elo 1237 vs rank 12, elo 1187), and mobileapps (rank 10, elo 1222 vs rank 24, elo 1172). GPT-5.2-Codex has a webapps score (elo 1125, rank 22) that GLM 5 wasn't measured against directly. GLM 5 also has scores across 12 separate model-arena categories (svg, dataviz, gamedev, 3d, etc.), a breadth GPT-5.2-Codex's benchmark set doesn't cover.
Context and modality differences
GPT-5.2-Codex handles a 400,000 token context window against GLM 5's 202,752 — a 1.97x ratio in GPT-5.2-Codex's favor, useful if you're feeding it entire large codebases in one shot. GPT-5.2-Codex also accepts image input alongside text; GLM 5 is text-only in and out. Max completion is capped at 128,000 tokens for GPT-5.2-Codex; GLM 5 has no stated max completion limit in its catalog record. If your workflow involves screenshots, diagrams, or visual debugging, GPT-5.2-Codex is the only option of the two.
Reasoning and control
GPT-5.2-Codex makes reasoning mandatory with four effort levels (xhigh, high, medium, low; default medium) — you can't turn it off, only tune it. GLM 5 treats reasoning as optional but on by default, giving you a toggle. GLM 5 also exposes a much larger parameter surface: frequency/presence penalties, logit_bias, logprobs, top_k, top_p, min_p, repetition_penalty — fine-grained sampling control GPT-5.2-Codex's parameter list doesn't include. If you're building an agent harness that needs to dial down verbosity or control repetition directly, GLM 5 gives you more knobs.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume agentic coding pipelines | GLM 5 | 4.6x cheaper per workload ($9.84 vs $45.50 on 10M in/2M out) and wins every shared agents benchmark category |
| Large codebase refactors needing full-repo context | GPT-5.2-Codex | 400K context window vs GLM 5's 202,752 — 1.97x more room |
| Debugging with screenshots or UI mockups | GPT-5.2-Codex | only one with image input modality; GLM 5 is text-only |
| Android-native app generation | GLM 5 | rank 6, elo 1244, 62% win rate vs GPT-5.2-Codex's rank 15, elo 1176, 47.5% |
| Fine-tuning sampling behavior (repetition, penalties) | GLM 5 | exposes top_k, top_p, min_p, repetition_penalty; GPT-5.2-Codex doesn't |
| Godot game dev tasks | GLM 5 | rank 3, elo 1237 vs GPT-5.2-Codex rank 12, elo 1187 |
Questions
- How much cheaper is GLM 5 than GPT-5.2-Codex?
- On a 10M input / 2M output token workload, GLM 5 costs $9.84 versus $45.50 for GPT-5.2-Codex — 4.6x less. The input price ratio is 2.92x ($0.60/M vs $1.75/M provider list), and output tokens widen the total gap since GPT-5.2-Codex charges $14.00/M against GLM 5's $1.92/M.
- Which model has a bigger context window?
- GPT-5.2-Codex supports 400,000 tokens of context versus GLM 5's 202,752 — a 1.97x ratio. GPT-5.2-Codex also caps completions at 128,000 tokens; GLM 5's catalog record shows no stated max completion limit.
- Does either model accept image input?
- Only GPT-5.2-Codex. Its input modalities are text and image, output is text-only. GLM 5 is text-to-text only, with no image support on either side.
- Which model wins on agentic coding benchmarks?
- GLM 5, on every Design Arena agents category both models share: androidnative (1244 vs 1176 elo), fullstack (1190 vs 1060), godotgamedev (1237 vs 1187), and mobileapps (1222 vs 1172). Both models are available through OpenKey with one API key and a flat 3% fee over provider list price.