Coding work
Code generation, repair, and software-engineering tasks
GPT-5.3 Codex
GPT-5.3 Codex leads on the public coding lane, 56.8 to 51.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GLM-5.1 vs GPT-5.3 Codex
Updated September 23, 2026. Rank says GPT-5.3 Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GPT-5.3 Codex has the higher public score estimate, 62.07 versus 57.71, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 7
- GLM-5.1 only
- 19
- GPT-5.3 Codex only
- 3
- Like-for-like categories
- 1 / 8
Supported: GLM-5.1 and GPT-5.3 CodexHow the comparison works
Which one for your work
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.
Long documents
Prompts that approach the documented context limit
GPT-5.3 Codex
GPT-5.3 Codex has the larger documented context window.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
GLM-5.1
GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GLM-5.1
GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 and GPT-5.3 Codex are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedCache-heavy agent loop cost
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-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Which one for a specific job
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.
51.5GLM-5.156.8GPT-5.3 Codex
Like-for-like · BenchAlign v5.6
GPT-5.3 Codex leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
19 GLM-5.1 only
7 Shared
3 GPT-5.3 Codex only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Shape of the matched evidence
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.
Terminal-Bench 2.0Agentic
Normalized gap 13.8GLM-5.1:63.5%GPT-5.3 Codex:77.3%LiveCodeBench (Vals)Coding
Normalized gap 5.9GLM-5.1:81.4%GPT-5.3 Codex:87.3%SWE-RebenchCoding
Normalized gap 4.5GLM-5.1:62.7%GPT-5.3 Codex:58.2%SWE-bench ProCoding
Normalized gap 1.6GLM-5.1:58.4%GPT-5.3 Codex:56.8%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.6 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.
Coding
Like-for-like- GLM-5.1
- 51.5
- Supported · #36/135
- GPT-5.3 Codex
- 56.8
- Supported · #23/135
- Basis
- BenchAlign v5.6 lane · 7 vs 6 public rows
- Reading
- GPT-5.3 Codex leads · intervals overlap
Agentic
Directional only- GLM-5.1
- 42.1
- Estimated · #43/105
- GPT-5.3 Codex
- 54.8
- Estimated · #30/105
- Basis
- BenchAlign v5.6 lane · 9 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5.1
- 50.2
- Supported · #54/160
- GPT-5.3 Codex
- 64.0
- Estimated · #25/160
- Basis
- BenchAlign v5.6 lane · 4 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5.1
- 92.4
- #4/124
- GPT-5.3 Codex
- 91.2
- #14/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.1
- 71.7
- Unranked · 2 rankable rows
- GPT-5.3 Codex
- 78.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not ranked
- GPT-5.3 Codex
- 75.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not ranked
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.1
- 63.8
- #3/7
- GPT-5.3 Codex
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.1 | GPT-5.3 Codex | Basis | Reading |
|---|---|---|---|---|
| Coding | 51.5Supported · #36/135 | 56.8Supported · #23/135 | Like-for-likeBenchAlign v5.6 lane · 7 vs 6 public rows | GPT-5.3 Codex leads · intervals overlap |
| Agentic | 42.1Estimated · #43/105 | 54.8Estimated · #30/105 | Directional onlyBenchAlign v5.6 lane · 9 vs 4 public rows | Directional only |
| Knowledge | 50.2Supported · #54/160 | 64.0Estimated · #25/160 | Directional onlyBenchAlign v5.6 lane · 4 vs 0 public rows | Directional only |
| Instruction following | 92.4#4/124 | 91.2#14/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 71.7Unranked · 2 rankable rows | 78.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 75.7Unranked · 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 | 63.8#3/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) 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.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- GLM-5.1
- $0.0036
- Fits in one request
- GPT-5.3 Codex
- $0.00875
- Fits in one request
GLM-5.1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5.1
- $0.0832
- Fits in one request
- GPT-5.3 Codex
- $0.1295
- Fits in one request
GLM-5.1 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5.1
- $0.352
- Does not fit in one request
- Cached input priced at the published list-input rate
- GPT-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.1GPT-5.3 Codex
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
GPT-5.3 Codex
API model ID
GLM-5.1
Not sourced
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
GPT-5.3 Codex
Not published
Documented inputs
GLM-5.1
Not sourced
GPT-5.3 Codex
text, image
OpenAI model catalogDocumented outputs
GLM-5.1
Not sourced
GPT-5.3 Codex
Provider availability
GLM-5.1
Not sourced
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GLM-5.1
Reasoning
GPT-5.3 Codex
Reasoning
Weight access
GLM-5.1
Open Weight
GPT-5.3 Codex
Proprietary
License
GLM-5.1
Open Weight
GPT-5.3 Codex
Proprietary
Release date
GLM-5.1
2026-04-07
GPT-5.3 Codex
2026-02-05
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- GPT-5.3 Codex has the higher public score estimate, 62.07 versus 57.71, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0832 vs $0.1295. Cache-heavy agent loop: $0.352 vs $0.525.
- Context tradeoff
- GPT-5.3 Codex has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5.1 or GPT-5.3 Codex?
GPT-5.3 Codex has the higher public score estimate, 62.07 versus 57.71, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GLM-5.1 or GPT-5.3 Codex?
GPT-5.3 Codex leads the public coding lane, 56.8 to 51.5, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GLM-5.1 or GPT-5.3 Codex?
GPT-5.3 Codex scores higher for agentic tasks on the public lane, 54.8 to 42.1. GLM-5.1 and GPT-5.3 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.
Which costs less, GLM-5.1 or GPT-5.3 Codex?
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.00875 on GPT-5.3 Codex; repository review costs $0.0832 and $0.1295; the cache-heavy agent loop costs $0.352 and $0.525. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5.1 or GPT-5.3 Codex?
GPT-5.3 Codex has the larger documented context window: 400K, compared with 203K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
GPT-5.3 Codex
API / mo$11,813
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence29 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceGPT-5.3 Codex77.3%SourceGPT-5.3 Codex leads this result
BrowseComp
GLM-5.168%SourceGPT-5.3 Codex—Not directly comparable
τ³-bench results
GLM-5.170.6%SourceGPT-5.3 Codex—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceGPT-5.3 Codex—Not directly comparable
CyberGym
GLM-5.168.7%SourceGPT-5.3 Codex—Not directly comparable
Claw-Eval
GLM-5.162.3%SourceGPT-5.3 Codex—Not directly comparable
Gert Labs
Shared sourceGLM-5.160.11%GPT-5.3 Codex57.47%GLM-5.1 leads this result
ResearchClawBench
GLM-5.118.2%SourceGPT-5.3 Codex—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%SourceGPT-5.3 Codex—Not directly comparable
OSWorld-Verified
GLM-5.1—GPT-5.3 Codex64.7%SourceNot directly comparable
JobBench
GLM-5.1—GPT-5.3 Codex33.7%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.158.4%SourceGPT-5.3 Codex56.8%SourceGLM-5.1 leads this result
NL2Repo
GLM-5.142.7%SourceGPT-5.3 Codex—Not directly comparable
SWE-Rebench
GLM-5.162.7%SourceGPT-5.3 Codex58.2%SourceGLM-5.1 leads this result
Vibe Code Bench
Shared sourceGLM-5.131.46%GPT-5.3 Codex61.77%GPT-5.3 Codex leads this result
OpenHarmony Bench
GLM-5.152.3%SourceGPT-5.3 Codex—Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%SourceGPT-5.3 Codex87.3%SourceGPT-5.3 Codex leads this result
SWE-bench (Vals)
GLM-5.176.4%SourceGPT-5.3 Codex78.0%SourceGPT-5.3 Codex leads this result
SWE-bench Verified
GLM-5.1—GPT-5.3 Codex85%SourceNot directly comparable
Knowledge
Math
AIME26
GLM-5.195.3%SourceGPT-5.3 Codex—Not directly comparable
HMMT Nov 2025
GLM-5.194.0%SourceGPT-5.3 Codex—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceGPT-5.3 Codex—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceGPT-5.3 Codex—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.133.448%SourceGPT-5.3 Codex—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.112.500%SourceGPT-5.3 Codex—Not directly comparable
29 public results · 7 shared
Watch GLM-5.1 vs GPT-5.3 Codex
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated September 23, 2026