Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Terra
GPT-5.6 Terra leads on the public coding lane, 64.7 to 39.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
GLM-4.7 vs GPT-5.6 Terra
Updated September 24, 2026. Rank says GPT-5.6 Terra 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-5.6 Terra has the higher public score, 72.58 versus 49.23, and the 90% score intervals do not overlap. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 8
- GLM-4.7 only
- 9
- GPT-5.6 Terra only
- 24
- Like-for-like categories
- 2 / 8
Supported: GLM-4.7 and GPT-5.6 TerraHow 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-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-4.7 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-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-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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.
39.6GLM-4.764.7GPT-5.6 Terra
Like-for-like · BenchAlign v5.7
GPT-5.6 Terra leads the like-for-like coding row.
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.
9 GLM-4.7 only
8 Shared
24 GPT-5.6 Terra only
2 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.
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 82.5GLM-4.7:2.439%GPT-5.6 Terra:84.900%FrontierMath v2 (Tier 4)Math
Normalized gap 68.3GLM-4.7:0.000%GPT-5.6 Terra:68.300%BrowseCompAgentic
Normalized gap 35.5GLM-4.7:52%GPT-5.6 Terra:87.5%GPQAKnowledge
Normalized gap 7.2GLM-4.7:85.7%GPT-5.6 Terra:92.9%MMLU-Pro (Vals)Knowledge
Normalized gap 4.0GLM-4.7:82.7%GPT-5.6 Terra:86.7%
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 v5.7 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-4.7
- 39.6
- Supported · #56/135
- GPT-5.6 Terra
- 64.7
- Supported · #8/135
- Basis
- BenchAlign v5.7 lane · 5 vs 8 public rows
- Reading
- GPT-5.6 Terra leads
Knowledge
Like-for-like- GLM-4.7
- 40.6
- Supported · #89/158
- GPT-5.6 Terra
- 71.1
- Supported · #9/158
- Basis
- BenchAlign v5.7 lane · 5 vs 8 public rows
- Reading
- GPT-5.6 Terra leads
Agentic
Directional only- GLM-4.7
- 27.0
- Estimated · #75/105
- GPT-5.6 Terra
- 59.5
- Supported · #18/105
- Basis
- BenchAlign v5.7 lane · 4 vs 9 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-4.7
- 81.4
- #51/124
- GPT-5.6 Terra
- 85.7
- #35/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-4.7
- 70.9
- Unranked · 2 rankable rows
- GPT-5.6 Terra
- 65.4
- #12/19
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-4.7
- Not ranked
- GPT-5.6 Terra
- 77.2
- #16/50
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-4.7
- Not ranked
- GPT-5.6 Terra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-4.7
- 25.8
- Unranked · 2 rankable rows
- GPT-5.6 Terra
- 96.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | GLM-4.7 | GPT-5.6 Terra | Basis | Reading |
|---|---|---|---|---|
| Coding | 39.6Supported · #56/135 | 64.7Supported · #8/135 | Like-for-likeBenchAlign v5.7 lane · 5 vs 8 public rows | GPT-5.6 Terra leads |
| Knowledge | 40.6Supported · #89/158 | 71.1Supported · #9/158 | Like-for-likeBenchAlign v5.7 lane · 5 vs 8 public rows | GPT-5.6 Terra leads |
| Agentic | 27.0Estimated · #75/105 | 59.5Supported · #18/105 | Directional onlyBenchAlign v5.7 lane · 4 vs 9 public rows | Directional only |
| Instruction following | 81.4#51/124 | 85.7#35/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 70.9Unranked · 2 rankable rows | 65.4#12/19 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multimodal | Not ranked | 77.2#16/50 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 25.8Unranked · 2 rankable rows | 96.8Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.6 Terra
- $0.008
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- GPT-5.6 Terra
- $0.136
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- GPT-5.6 Terra
- $0.2
- Fits in one request
GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token 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-4.7GPT-5.6 Terra
Context window
Maximum documented context; output-token limits may be lower.
GLM-4.7
200K
GPT-5.6 Terra
1.05M
OpenAI model catalogAPI model ID
GLM-4.7
Not sourced
GPT-5.6 Terra
gpt-5.6-terra
OpenAI model catalogCached-input rate
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-5.6 Terra
$0.2 per 1M cached input tokens
OpenAI pricingDocumented inputs
GLM-4.7
Not sourced
GPT-5.6 Terra
text, image
OpenAI model catalogDocumented outputs
GLM-4.7
Not sourced
GPT-5.6 Terra
Provider availability
GLM-4.7
Not sourced
GPT-5.6 Terra
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GLM-4.7
Reasoning
GPT-5.6 Terra
Reasoning
Weight access
GLM-4.7
Open Weight
GPT-5.6 Terra
Proprietary
License
GLM-4.7
Open Weight
GPT-5.6 Terra
Proprietary
Release date
GLM-4.7
2025-10-01
GPT-5.6 Terra
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 Terra has the higher public score, 72.58 versus 49.23, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.6 Terra has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-4.7 or GPT-5.6 Terra?
GPT-5.6 Terra has the higher public score, 72.58 versus 49.23, 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-4.7 or GPT-5.6 Terra?
GPT-5.6 Terra leads the public coding lane, 64.7 to 39.6, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GLM-4.7 or GPT-5.6 Terra?
GPT-5.6 Terra scores higher for agentic tasks on the public lane, 59.5 to 27. GLM-4.7 is 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-4.7 or GPT-5.6 Terra?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GLM-4.7 or GPT-5.6 Terra?
GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 200K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence41 rows
Agentic
Terminal-Bench 2.0
GLM-4.741%SourceGPT-5.6 Terra—Not directly comparable
BrowseComp
GLM-4.752%SourceGPT-5.6 Terra87.5%SourceGPT-5.6 Terra leads this result
VITA-Bench
GLM-4.715.5%SourceGPT-5.6 Terra—Not directly comparable
Gert Labs
GLM-4.739.95%SourceGPT-5.6 Terra—Not directly comparable
Terminal-Bench 3.0
GLM-4.7—GPT-5.6 Terra20.8%SourceNot directly comparable
Terminal-Bench 2.1
GLM-4.7—GPT-5.6 Terra87.4%SourceNot directly comparable
OSWorld 2.0
GLM-4.7—GPT-5.6 Terra50.2%SourceNot directly comparable
CyberGym
GLM-4.7—GPT-5.6 Terra81.8%SourceNot directly comparable
ExploitGym
GLM-4.7—GPT-5.6 Terra23.2%SourceNot directly comparable
Toolathlon
GLM-4.7—GPT-5.6 Terra53.1%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GLM-4.7—GPT-5.6 Terra77.5%SourceNot directly comparable
ApprenticeBench
GLM-4.7—GPT-5.6 Terra16%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-4.773.8%SourceGPT-5.6 Terra—Not directly comparable
LiveCodeBench
GLM-4.784.9%SourceGPT-5.6 Terra—Not directly comparable
SWE-Rebench
GLM-4.758.7%SourceGPT-5.6 Terra—Not directly comparable
LiveCodeBench (Vals)
GLM-4.782.2%SourceGPT-5.6 Terra85.9%SourceGPT-5.6 Terra leads this result
SWE-bench (Vals)
GLM-4.769.4%SourceGPT-5.6 Terra95.4%SourceGPT-5.6 Terra leads this result
SWE-bench Pro
GLM-4.7—GPT-5.6 Terra63.4%SourceNot directly comparable
Terminal-Bench 2.1
GLM-4.7—GPT-5.6 Terra87.4%SourceNot directly comparable
DeepSWE
GLM-4.7—GPT-5.6 Terra69.6%SourceNot directly comparable
FrontierCode 1.1 Extended
GLM-4.7—GPT-5.6 Terra55.8%SourceNot directly comparable
cursorBench32
GLM-4.7—GPT-5.6 Terra64.9%SourceNot directly comparable
VulcanBench v3
GLM-4.7—GPT-5.6 Terra87.0%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GLM-4.785.7%SourceGPT-5.6 Terra92.9%SourceGPT-5.6 Terra leads this result
MMLU-Pro
GLM-4.784.3%SourceGPT-5.6 Terra—Not directly comparable
HLE
GLM-4.724.8%SourceGPT-5.6 Terra—Not directly comparable
GPQA Diamond (Vals)
GLM-4.780.1%SourceGPT-5.6 Terra90.9%SourceGPT-5.6 Terra leads this result
MMLU-Pro (Vals)
GLM-4.782.7%SourceGPT-5.6 Terra86.7%SourceGPT-5.6 Terra leads this result
GPQA-D
GLM-4.7—GPT-5.6 Terra92.9%SourceNot directly comparable
HLE-Verified
GLM-4.7—GPT-5.6 Terra51.1%SourceNot directly comparable
LABBench2
GLM-4.7—GPT-5.6 Terra81.2%SourceNot directly comparable
HealthBench Professional
GLM-4.7—GPT-5.6 Terra57.7%SourceNot directly comparable
HealthBench Hard
GLM-4.7—GPT-5.6 Terra32.7%SourceNot directly comparable
Math
AIME 2025
GLM-4.795.7%SourceGPT-5.6 Terra—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-4.72.439%SourceGPT-5.6 Terra84.900%SourceGPT-5.6 Terra leads this result
FrontierMath v2 (Tier 4)
GLM-4.70.000%SourceGPT-5.6 Terra68.300%SourceGPT-5.6 Terra leads this result
FrontierMath (legacy)
GLM-4.7—GPT-5.6 Terra84.9%SourceNot directly comparable
41 public results · 8 shared
Watch GLM-4.7 vs GPT-5.6 Terra
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 24, 2026