Coding
Like-for-like- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- GLM-5.2
- 60.9
- Supported · #20/183
- Basis
- BenchAlign lane · 4 vs 8 public rows
- Reading
- GLM-5.2 leads
Gemini 3.5 Flash-Lite vs GLM-5.2
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5.2 has the higher public score estimate, 68.22 versus 60.5, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
8 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-5.2
GLM-5.2 leads on the public coding lane, 60.9 to 43.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.5 Flash-Lite is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Gemini 3.5 Flash-Lite only
8 Shared
17 GLM-5.2 only
- Shared results
- 8
- Gemini 3.5 Flash-Lite only
- 2
- GLM-5.2 only
- 17
- 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-5.2
- 60.9
- Supported · #39/181
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- GLM-5.2 leads · intervals overlap
Agentic
Directional only- Gemini 3.5 Flash-Lite
- 43.4
- Estimated · #104/151
- GLM-5.2
- 60.2
- Supported · #25/151
- Basis
- BenchAlign lane · 3 vs 6 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.5 Flash-Lite
- 60.8
- Unranked · 3 rankable rows
- GLM-5.2
- 76.2
- 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-5.2
- 80.7
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GLM-5.2
- 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-5.2
- 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-5.2
- 89.6
- #22/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash-Lite | GLM-5.2 | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.6Supported · #121/183 | 60.9Supported · #20/183 | Like-for-likeBenchAlign lane · 4 vs 8 public rows | GLM-5.2 leads |
| Knowledge | 53.0Supported · #71/181 | 60.9Supported · #39/181 | Like-for-likeBenchAlign lane · 2 vs 6 public rows | GLM-5.2 leads · intervals overlap |
| Agentic | 43.4Estimated · #104/151 | 60.2Supported · #25/151 | Directional onlyBenchAlign lane · 3 vs 6 public rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | 76.2Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 80.7Unranked · 4 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 | 89.6#22/120 | 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-5.2: 81%Normalized gap 27.0Gemini 3.5 Flash-Lite source GLM-5.2 sourceLiveCodeBench (Vals)
Coding
Gemini 3.5 Flash-Lite: 79.0%GLM-5.2: 69.5%Normalized gap 9.5Gemini 3.5 Flash-Lite source GLM-5.2 sourceSWE-bench Pro
Coding
Gemini 3.5 Flash-Lite: 54.2%GLM-5.2: 62.1%Normalized gap 7.9Gemini 3.5 Flash-Lite source GLM-5.2 sourceMMLU-Pro (Vals)
Knowledge
Gemini 3.5 Flash-Lite: 85.8%GLM-5.2: 86.7%Normalized gap 0.9Gemini 3.5 Flash-Lite source GLM-5.2 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-5.2
- $0.0036
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.5 Flash-Lite
- $0.0225
- Fits in one request
- GLM-5.2
- $0.0832
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.5 Flash-Lite
- $0.037
- Fits in one request
- GLM-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
Gemini 3.5 Flash-Lite has the lower modeled cost
GLM-5.2 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.
SpecificationGemini 3.5 Flash-LiteGLM-5.2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash-Lite
GLM-5.2
1M
API model ID
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationGLM-5.2
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-5.2
Not published
Documented inputs
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationGLM-5.2
Not sourced
Documented outputs
Gemini 3.5 Flash-Lite
GLM-5.2
Not sourced
Provider availability
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideGLM-5.2
Not sourced
Reasoning profile
Gemini 3.5 Flash-Lite
Reasoning
GLM-5.2
Reasoning
Weight access
Gemini 3.5 Flash-Lite
Proprietary
GLM-5.2
Open Weight
License
Gemini 3.5 Flash-Lite
Proprietary
GLM-5.2
Open Weight
Release date
Gemini 3.5 Flash-Lite
2026-07-21
GLM-5.2
2026-06-16
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-5.2 has the higher public score estimate, 68.22 versus 60.5, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0225 vs $0.0832. Cache-heavy agent loop: $0.037 vs $0.352.
- Context tradeoff
- Both models list 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 evidence27 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54%SourceGLM-5.281%SourceGLM-5.2 leads this result
OSWorld-Verified
Gemini 3.5 Flash-Lite74%SourceGLM-5.2—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%SourceGLM-5.267.8%SourceGLM-5.2 leads this result
Terminal-Bench 3.0
Gemini 3.5 Flash-Lite—GLM-5.24.6%SourceNot directly comparable
MCP Atlas
Gemini 3.5 Flash-Lite—GLM-5.276.8%SourceNot directly comparable
Toolathlon
Gemini 3.5 Flash-Lite—GLM-5.248.2%SourceNot directly comparable
ResearchClawBench
Gemini 3.5 Flash-Lite—GLM-5.220.7%SourceNot directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%SourceGLM-5.281.0%SourceGLM-5.2 leads this result
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%SourceGLM-5.262.1%SourceGLM-5.2 leads this result
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%SourceGLM-5.269.5%SourceGemini 3.5 Flash-Lite leads this result
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%SourceGLM-5.282.8%SourceGLM-5.2 leads this result
NL2Repo
Gemini 3.5 Flash-Lite—GLM-5.248.9%SourceNot directly comparable
ProgramBench
Gemini 3.5 Flash-Lite—GLM-5.263.7%SourceNot directly comparable
cursorBench32
Gemini 3.5 Flash-Lite—GLM-5.255.0%SourceNot directly comparable
OpenHarmony Bench
Gemini 3.5 Flash-Lite—GLM-5.258.4%SourceNot directly comparable
Reasoning
Knowledge
GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%SourceGLM-5.285.6%SourceGLM-5.2 leads this result
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%SourceGLM-5.286.7%SourceGLM-5.2 leads this result
GPQA
Gemini 3.5 Flash-Lite—GLM-5.291.2%SourceNot directly comparable
GPQA-D
Gemini 3.5 Flash-Lite—GLM-5.291.2%SourceNot directly comparable
HLE
Gemini 3.5 Flash-Lite—GLM-5.254.7%SourceNot directly comparable
HLE w/o tools
Gemini 3.5 Flash-Lite—GLM-5.240.5%SourceNot directly comparable
Math
AIME26
Gemini 3.5 Flash-Lite—GLM-5.299.2%SourceNot directly comparable
HMMT Nov 2025
Gemini 3.5 Flash-Lite—GLM-5.294.4%SourceNot directly comparable
HMMT Feb 2026
Gemini 3.5 Flash-Lite—GLM-5.292.5%SourceNot directly comparable
MMAnswerBench
Gemini 3.5 Flash-Lite—GLM-5.291.0%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.5 Flash-Lite or GLM-5.2?
GLM-5.2 has the higher public score estimate, 68.22 versus 60.5, 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-5.2?
GLM-5.2 leads the public coding lane, 60.9 to 43.6, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GLM-5.2?
GLM-5.2 scores higher for agentic tasks on the public lane, 60.2 to 43.4. Gemini 3.5 Flash-Lite 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, Gemini 3.5 Flash-Lite or GLM-5.2?
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.0036 on GLM-5.2; repository review costs $0.0225 and $0.0832; the cache-heavy agent loop costs $0.037 and $0.352. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3.5 Flash-Lite or GLM-5.2?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
Watch Gemini 3.5 Flash-Lite vs GLM-5.2
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.