Updated October 17, 2026. Rank says GPT-5.2-Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-5.2-Codex has the higher public point estimate, 55.3 versus 48.85. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Code generation, repair, and software-engineering tasks
GPT-5.2-Codex
GPT-5.2-Codex has the higher public coding point estimate, 43.7 to 37.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.2-Codex
GPT-5.2-Codex has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-4.7 and GPT-5.2-Codex are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 has no comparable published API token rate.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GPT-5.2-Codex has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
LiveCodeBench (Vals)Coding
Normalized gap 5.8Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | GLM-4.7 | GPT-5.2-Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 37.2Supported · #72/150 | 43.7Supported · #56/150 | Like-for-likeBenchAlign v5.8 lane · 6 vs 3 public rows | GPT-5.2-Codex leads · intervals overlap |
| Agentic | 31.6Estimated · #91/128 | 40.8Estimated · #64/128 | Directional onlyBenchAlign v5.8 lane · 4 vs 2 public rows | Directional only |
| Knowledge | 44.1Supported · #100/184 | 56.4Estimated · #54/184 | Directional onlyBenchAlign v5.8 lane · 5 vs 0 public rows | Directional only |
| Instruction following | 82.8#54/148 | 93.8#4/148 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 74.6Unranked · 2 rankable rows | 82.4Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 76.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
GLM-4.7 has no comparable published API token rate.
50K fresh input + 3K output tokens
GLM-4.7 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GLM-4.7 does not fit this workload in one request. GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-4.7
200K
GPT-5.2-Codex
400K
GLM-4.7
Not sourced
GPT-5.2-Codex
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-4.7
No comparable hosted API rate
GPT-5.2-Codex
Not published
GLM-4.7
Not sourced
GPT-5.2-Codex
Not sourced
GLM-4.7
Not sourced
GPT-5.2-Codex
Not sourced
GLM-4.7
Not sourced
GPT-5.2-Codex
Not sourced
GLM-4.7
Reasoning
GPT-5.2-Codex
Reasoning
GLM-4.7
Open Weight
GPT-5.2-Codex
Proprietary
GLM-4.7
Open Weight
GPT-5.2-Codex
Proprietary
GLM-4.7
2025-12-22
GPT-5.2-Codex
2025-12-18
GPT-5.2-Codex has the higher public point estimate, 55.3 versus 48.85. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.2-Codex has the higher public coding point estimate, 43.7 to 37.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.2-Codex scores higher for agentic tasks on the public lane, 40.8 to 31.6. GLM-4.7 and GPT-5.2-Codex are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
GPT-5.2-Codex has the larger documented context window: 400K, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
VITA-Bench
Not directly comparable
Gert Labs
Shared sourceGPT-5.2-Codex leads this result
JobBench
Not directly comparable
SWE-bench Verified
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-Rebench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
LiveCodeBench (Vals)
GPT-5.2-Codex leads this result
SWE-bench (Vals)
GPT-5.2-Codex leads this result
Vibe Code Bench
Not directly comparable
GPQA
Not directly comparable
MMLU-Pro
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated October 17, 2026
