Agentic
Directional only- GLM-5.1
- 50.1
- Estimated · #63/151
- GPT-5.5 Pro
- 60.6
- Estimated · #24/151
- Basis
- BenchAlign lane · 9 vs 1 public rows
- Reading
- Directional only
GLM-5.1 vs GPT-5.5 Pro
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5.1 has the higher public score estimate, 64.41 versus 63.07, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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-5.5 Pro
GPT-5.5 Pro has the larger documented context window.
Confidence: documented
Chat 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-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.5 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 and GPT-5.5 Pro are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Cache-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-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
22 GLM-5.1 only
4 Shared
2 GPT-5.5 Pro only
- Shared results
- 4
- GLM-5.1 only
- 22
- GPT-5.5 Pro only
- 2
- Like-for-like categories
- 0 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Knowledge
Directional only- GLM-5.1
- 55.4
- Supported · #59/181
- GPT-5.5 Pro
- 61.1
- Estimated · #36/181
- Basis
- BenchAlign lane · 4 vs 2 public rows
- Reading
- Directional only
Coding
Not comparable- GLM-5.1
- 56.8
- Supported · #39/183
- GPT-5.5 Pro
- Not ranked
- Basis
- BenchAlign lane · 7 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.1
- 69.9
- Unranked · 2 rankable rows
- GPT-5.5 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.1
- 64.1
- #3/7
- GPT-5.5 Pro
- 70.2
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 4 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not ranked
- GPT-5.5 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not ranked
- GPT-5.5 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.1
- 93.5
- #5/120
- GPT-5.5 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.1 | GPT-5.5 Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.1Estimated · #63/151 | 60.6Estimated · #24/151 | Directional onlyBenchAlign lane · 9 vs 1 public rows | Directional only |
| Knowledge | 55.4Supported · #59/181 | 61.1Estimated · #36/181 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Coding | 56.8Supported · #39/183 | Not ranked | Not comparableBenchAlign lane · 7 vs 0 public rows | Not comparable |
| Reasoning | 69.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 64.1#3/7 | 70.2Unranked · 3 rankable rows | Not comparableProvisional lane · 4 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.5#5/120 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
FrontierMath v2 (Tier 4)
Math
BrowseComp
Agentic
FrontierMath v2 (Tiers 1-3)
Math
HLE
Knowledge
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-5.5 Pro
- $0.12
- 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-5.5 Pro
- $2.04
- 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-5.5 Pro
- $8.40
- 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-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.1GPT-5.5 Pro
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
GPT-5.5 Pro
API model ID
GLM-5.1
Not sourced
GPT-5.5 Pro
gpt-5.5-pro
OpenAI GPT-5.5 Pro 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-5.5 Pro
Not published
OpenAI pricingDocumented inputs
GLM-5.1
Not sourced
GPT-5.5 Pro
text, image
OpenAI model catalogDocumented outputs
GLM-5.1
Not sourced
GPT-5.5 Pro
Provider availability
GLM-5.1
Not sourced
GPT-5.5 Pro
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GLM-5.1
Reasoning
GPT-5.5 Pro
Reasoning
Weight access
GLM-5.1
Open Weight
GPT-5.5 Pro
Proprietary
License
GLM-5.1
Open Weight
GPT-5.5 Pro
Proprietary
Release date
GLM-5.1
2026-04-07
GPT-5.5 Pro
2026-04-23
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
- GLM-5.1 has the higher public score estimate, 64.41 versus 63.07, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0832 vs $2.04. Cache-heavy agent loop: $0.352 vs $8.40.
- Context tradeoff
- GPT-5.5 Pro has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
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-5.5 Pro
API / mo$157,500
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 evidence28 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceGPT-5.5 Pro—Not directly comparable
BrowseComp
GLM-5.168%SourceGPT-5.5 Pro90.1%SourceGPT-5.5 Pro leads this result
τ³-bench results
GLM-5.170.6%SourceGPT-5.5 Pro—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceGPT-5.5 Pro—Not directly comparable
CyberGym
GLM-5.168.7%SourceGPT-5.5 Pro—Not directly comparable
Claw-Eval
GLM-5.162.3%SourceGPT-5.5 Pro—Not directly comparable
Gert Labs
GLM-5.160.11%SourceGPT-5.5 Pro—Not directly comparable
ResearchClawBench
GLM-5.118.2%SourceGPT-5.5 Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%SourceGPT-5.5 Pro—Not directly comparable
Coding
SWE-bench Pro
GLM-5.158.4%SourceGPT-5.5 Pro—Not directly comparable
NL2Repo
GLM-5.142.7%SourceGPT-5.5 Pro—Not directly comparable
SWE-Rebench
GLM-5.162.7%SourceGPT-5.5 Pro—Not directly comparable
Vibe Code Bench
GLM-5.131.46%SourceGPT-5.5 Pro—Not directly comparable
OpenHarmony Bench
GLM-5.152.3%SourceGPT-5.5 Pro—Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%SourceGPT-5.5 Pro—Not directly comparable
SWE-bench (Vals)
GLM-5.176.4%SourceGPT-5.5 Pro—Not directly comparable
Knowledge
GPQA-D
GLM-5.186.2%SourceGPT-5.5 Pro—Not directly comparable
HLE
GLM-5.152.3%SourceGPT-5.5 Pro57.2%SourceGPT-5.5 Pro leads this result
GPQA Diamond (Vals)
GLM-5.184.5%SourceGPT-5.5 Pro—Not directly comparable
MMLU-Pro (Vals)
GLM-5.186.9%SourceGPT-5.5 Pro—Not directly comparable
HLE w/o tools
GLM-5.1—GPT-5.5 Pro43.1%SourceNot directly comparable
Math
AIME26
GLM-5.195.3%SourceGPT-5.5 Pro—Not directly comparable
HMMT Nov 2025
GLM-5.194.0%SourceGPT-5.5 Pro—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceGPT-5.5 Pro—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceGPT-5.5 Pro—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGLM-5.133.448%GPT-5.5 Pro51.000%GPT-5.5 Pro leads this result
FrontierMath v2 (Tier 4)
Shared sourceGLM-5.112.500%GPT-5.5 Pro39.600%GPT-5.5 Pro leads this result
FrontierMath (legacy)
GLM-5.1—GPT-5.5 Pro52.4%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.1 or GPT-5.5 Pro?
GLM-5.1 has the higher public score estimate, 64.41 versus 63.07, 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-5.5 Pro?
GPT-5.5 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GLM-5.1 or GPT-5.5 Pro?
GPT-5.5 Pro scores higher for agentic tasks on the public lane, 60.6 to 50.1. GLM-5.1 and GPT-5.5 Pro 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-5.5 Pro?
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.12 on GPT-5.5 Pro; repository review costs $0.0832 and $2.04; the cache-heavy agent loop costs $0.352 and $8.40. 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-5.5 Pro 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-5.5 Pro?
GPT-5.5 Pro has the larger documented context window: 1.05M, compared with 203K.
Related comparisons
Last updated September 4, 2026
Watch GLM-5.1 vs GPT-5.5 Pro
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.