Coding
Like-for-like- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- GLM-4.7
- 48.0
- Supported · #86/183
- Basis
- BenchAlign lane · 4 vs 3 public rows
- Reading
- GLM-4.7 leads · intervals overlap
Gemini 3.5 Flash-Lite vs GLM-4.7
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.5 Flash-Lite has the higher public score estimate, 60.5 versus 58.99, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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.
Coding work
Code generation, repair, and software-engineering tasks
GLM-4.7
GLM-4.7 leads on the public coding lane, 48 to 43.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite 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
Gemini 3.5 Flash-Lite and GLM-4.7 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat 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-rates
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-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
Confidence: listed-rates
Repository 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
9 Gemini 3.5 Flash-Lite only
1 Shared
12 GLM-4.7 only
- Shared results
- 1
- Gemini 3.5 Flash-Lite only
- 9
- GLM-4.7 only
- 12
- Like-for-like categories
- 2 / 8
1 category rests 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
Like-for-like- Gemini 3.5 Flash-Lite
- 53.0
- Supported · #71/181
- GLM-4.7
- 48.2
- Supported · #102/181
- Basis
- BenchAlign lane · 2 vs 3 public rows
- Reading
- Gemini 3.5 Flash-Lite leads · intervals overlap
Agentic
Directional only- Gemini 3.5 Flash-Lite
- 43.4
- Estimated · #104/151
- GLM-4.7
- 51.1
- Estimated · #57/151
- Basis
- BenchAlign lane · 3 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.5 Flash-Lite
- 60.8
- Unranked · 3 rankable rows
- GLM-4.7
- 69.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GLM-4.7
- 26.0
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GLM-4.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.5 Flash-Lite
- 76.1
- #16/48
- GLM-4.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GLM-4.7
- 82.6
- #49/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash-Lite | GLM-4.7 | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.6Supported · #121/183 | 48.0Supported · #86/183 | Like-for-likeBenchAlign lane · 4 vs 3 public rows | GLM-4.7 leads · intervals overlap |
| Knowledge | 53.0Supported · #71/181 | 48.2Supported · #102/181 | Like-for-likeBenchAlign lane · 2 vs 3 public rows | Gemini 3.5 Flash-Lite leads · intervals overlap |
| Agentic | 43.4Estimated · #104/151 | 51.1Estimated · #57/151 | Directional onlyBenchAlign lane · 3 vs 4 public rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | 69.9Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 26.0Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.1#16/48 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 82.6#49/120 | 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.
Terminal-Bench 2.0
Agentic
Gemini 3.5 Flash-Lite: 54%GLM-4.7: 41%Normalized gap 13.0Gemini 3.5 Flash-Lite source GLM-4.7 source
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
- Gemini 3.5 Flash-Lite
- $0.00155
- Fits in one request
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.5 Flash-Lite
- $0.0225
- Fits in one request
- GLM-4.7
- Self-hosted; infrastructure cost varies
- 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
- Gemini 3.5 Flash-Lite
- $0.037
- Fits in one request
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.5 Flash-LiteGLM-4.7
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash-Lite
GLM-4.7
200K
API model ID
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationGLM-4.7
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingGLM-4.7
No comparable hosted API rate
Documented inputs
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationGLM-4.7
Not sourced
Documented outputs
Gemini 3.5 Flash-Lite
GLM-4.7
Not sourced
Provider availability
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideGLM-4.7
Not sourced
Reasoning profile
Gemini 3.5 Flash-Lite
Reasoning
GLM-4.7
Reasoning
Weight access
Gemini 3.5 Flash-Lite
Proprietary
GLM-4.7
Open Weight
License
Gemini 3.5 Flash-Lite
Proprietary
GLM-4.7
Open Weight
Release date
Gemini 3.5 Flash-Lite
2026-07-21
GLM-4.7
2025-10-01
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
- Gemini 3.5 Flash-Lite has the higher public score estimate, 60.5 versus 58.99, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Gemini 3.5 Flash-Lite has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence22 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54%SourceGLM-4.741%SourceGemini 3.5 Flash-Lite leads this result
OSWorld-Verified
Gemini 3.5 Flash-Lite74%SourceGLM-4.7—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%SourceGLM-4.7—Not directly comparable
BrowseComp
Gemini 3.5 Flash-Lite—GLM-4.752%SourceNot directly comparable
VITA-Bench
Gemini 3.5 Flash-Lite—GLM-4.715.5%SourceNot directly comparable
Gert Labs
Gemini 3.5 Flash-Lite—GLM-4.739.95%SourceNot directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%SourceGLM-4.7—Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%SourceGLM-4.7—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%SourceGLM-4.7—Not directly comparable
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%SourceGLM-4.7—Not directly comparable
SWE-bench Verified
Gemini 3.5 Flash-Lite—GLM-4.773.8%SourceNot directly comparable
LiveCodeBench
Gemini 3.5 Flash-Lite—GLM-4.784.9%SourceNot directly comparable
SWE-Rebench
Gemini 3.5 Flash-Lite—GLM-4.758.7%SourceNot directly comparable
Reasoning
MRCRv2
Gemini 3.5 Flash-Lite72.2%SourceGLM-4.7—Not directly comparable
Knowledge
GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%SourceGLM-4.7—Not directly comparable
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%SourceGLM-4.7—Not directly comparable
GPQA
Gemini 3.5 Flash-Lite—GLM-4.785.7%SourceNot directly comparable
MMLU-Pro
Gemini 3.5 Flash-Lite—GLM-4.784.3%SourceNot directly comparable
HLE
Gemini 3.5 Flash-Lite—GLM-4.724.8%SourceNot directly comparable
Math
Frequently asked questions
Which is better, Gemini 3.5 Flash-Lite or GLM-4.7?
Gemini 3.5 Flash-Lite has the higher public score estimate, 60.5 versus 58.99, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 3.5 Flash-Lite or GLM-4.7?
GLM-4.7 leads the public coding lane, 48 to 43.6, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GLM-4.7?
GLM-4.7 scores higher for agentic tasks on the public lane, 51.1 to 43.4. Gemini 3.5 Flash-Lite and GLM-4.7 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, Gemini 3.5 Flash-Lite or GLM-4.7?
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, Gemini 3.5 Flash-Lite or GLM-4.7?
Gemini 3.5 Flash-Lite has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated September 4, 2026
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