Agentic
Like-for-like- GLM-5.1
- 65.4
- GPT-5.6 Sol
- 92.0
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.6 Sol leads
Model comparison
GLM-5.1 vs GPT-5.6 Sol
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Sol has the higher public score, 81.48 versus 66.89, and the 90% score intervals do not overlap.
7 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the same 2 weighted benchmark rows.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol 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
The category averages use different weighted benchmark sets, so they are 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.
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.
13 GLM-5.1 only
7 Shared
17 GPT-5.6 Sol only
- Shared results
- 7
- GLM-5.1 only
- 13
- GPT-5.6 Sol only
- 17
- Like-for-like categories
- 1 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- GLM-5.1
- 61.3
- GPT-5.6 Sol
- 64.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Math
Directional only- GLM-5.1
- 62.0
- GPT-5.6 Sol
- 87.5
- Weighted basis
- 4 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.1
- Not measured
- GPT-5.6 Sol
- 92.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.1
- 52.3
- GPT-5.6 Sol
- 94.6
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not measured
- GPT-5.6 Sol
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not measured
- GPT-5.6 Sol
- 83.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.1
- Not measured
- GPT-5.6 Sol
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.1 | GPT-5.6 Sol | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 65.4 | 92.0 | Like-for-like2 vs 2 rows | GPT-5.6 Sol leads |
| Coding | 61.3 | 64.6 | Directional only2 vs 1 rows | Directional only |
| Math | 62.0 | 87.5 | Directional only4 vs 2 rows | Directional only |
| Reasoning | Not measured | 92.5 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | 52.3 | 94.6 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 83.0 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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
FrontierMath v2 (Tiers 1-3)
Math
Terminal-Bench 2.0
Agentic
BrowseComp
Agentic
SWE-bench Pro
Coding
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-5.6 Sol
- $0.02
- 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-5.6 Sol
- $0.34
- 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-5.6 Sol
- $0.5
- Fits 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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.1GPT-5.6 Sol
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
GPT-5.6 Sol
1.05M
OpenAI model catalogAPI model ID
GLM-5.1
Not sourced
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogCached-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-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
GLM-5.1
Not sourced
GPT-5.6 Sol
text, image
OpenAI model catalogDocumented outputs
GLM-5.1
Not sourced
GPT-5.6 Sol
Provider availability
GLM-5.1
Not sourced
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GLM-5.1
Reasoning
GPT-5.6 Sol
Reasoning
Weight access
GLM-5.1
Open Weight
GPT-5.6 Sol
Proprietary
License
GLM-5.1
Open Weight
GPT-5.6 Sol
Proprietary
Release date
GLM-5.1
2026-04-07
GPT-5.6 Sol
2026-07-09
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-5.6 Sol has the higher public score, 81.48 versus 66.89, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0832 vs $0.34. Cache-heavy agent loop: $0.352 vs $0.5.
- Context tradeoff
- GPT-5.6 Sol 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-5.6 Sol
API / mo$26,250
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 evidence37 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceGPT-5.6 Sol91.9%SourceGPT-5.6 Sol leads this result
BrowseComp
GLM-5.168%SourceGPT-5.6 Sol92.2%SourceGPT-5.6 Sol leads this result
τ³-bench results
GLM-5.170.6%SourceGPT-5.6 Sol—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceGPT-5.6 Sol—Not directly comparable
CyberGym
GLM-5.168.7%SourceGPT-5.6 Sol84.5%SourceGPT-5.6 Sol leads this result
Claw-Eval
GLM-5.162.3%SourceGPT-5.6 Sol—Not directly comparable
Gert Labs
GLM-5.160.11%SourceGPT-5.6 Sol—Not directly comparable
ResearchClawBench
GLM-5.118.2%SourceGPT-5.6 Sol—Not directly comparable
OSWorld 2.0
GLM-5.1—GPT-5.6 Sol62.6%SourceNot directly comparable
ExploitGym
GLM-5.1—GPT-5.6 Sol33.7%SourceNot directly comparable
Toolathlon
GLM-5.1—GPT-5.6 Sol58%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.158.4%SourceGPT-5.6 Sol64.6%SourceGPT-5.6 Sol leads this result
NL2Repo
GLM-5.142.7%SourceGPT-5.6 Sol—Not directly comparable
SWE-Rebench
GLM-5.162.7%SourceGPT-5.6 Sol—Not directly comparable
Vibe Code Bench
GLM-5.131.46%SourceGPT-5.6 Sol—Not directly comparable
Terminal-Bench 2.0
GLM-5.1—GPT-5.6 Sol91.9%SourceNot directly comparable
deepSwe
GLM-5.1—GPT-5.6 Sol72.7%SourceNot directly comparable
FrontierCode 1.1 Extended
GLM-5.1—GPT-5.6 Sol60.6%SourceNot directly comparable
cursorBench32
GLM-5.1—GPT-5.6 Sol67.2%SourceNot directly comparable
VulcanBench v3
GLM-5.1—GPT-5.6 Sol87.0%SourceNot directly comparable
Reasoning
Knowledge
GPQA-D
GLM-5.186.2%SourceGPT-5.6 Sol94.6%SourceGPT-5.6 Sol leads this result
HLE
GLM-5.152.3%SourceGPT-5.6 Sol—Not directly comparable
GPQA
GLM-5.1—GPT-5.6 Sol94.6%SourceNot directly comparable
HealthBench Professional
GLM-5.1—GPT-5.6 Sol60.5%SourceNot directly comparable
HealthBench Hard
GLM-5.1—GPT-5.6 Sol33.1%SourceNot directly comparable
Math
AIME26
GLM-5.195.3%SourceGPT-5.6 Sol—Not directly comparable
HMMT Nov 2025
GLM-5.194.0%SourceGPT-5.6 Sol—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceGPT-5.6 Sol—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceGPT-5.6 Sol—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.133.448%SourceGPT-5.6 Sol89.000%SourceGPT-5.6 Sol leads this result
FrontierMath v2 (Tier 4)
GLM-5.112.500%SourceGPT-5.6 Sol83.000%SourceGPT-5.6 Sol leads this result
FrontierMath (legacy)
GLM-5.1—GPT-5.6 Sol89%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, GLM-5.1 or GPT-5.6 Sol?
GPT-5.6 Sol has the higher public score, 81.48 versus 66.89, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GLM-5.1 or GPT-5.6 Sol?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5.1 or GPT-5.6 Sol?
GPT-5.6 Sol leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Which costs less, GLM-5.1 or GPT-5.6 Sol?
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.02 on GPT-5.6 Sol; repository review costs $0.0832 and $0.34; the cache-heavy agent loop costs $0.352 and $0.5. 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.
Which has the larger context window, GLM-5.1 or GPT-5.6 Sol?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 203K.
Related comparisons
Last updated August 10, 2026
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