Coding
Like-for-like- Gemini 3.6 Flash
- 58.9
- Supported · #30/183
- GLM-5.1
- 56.8
- Supported · #39/183
- Basis
- BenchAlign lane · 4 vs 7 public rows
- Reading
- Gemini 3.6 Flash leads · intervals overlap
Gemini 3.6 Flash vs GLM-5.1
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.6 Flash has the higher public score estimate, 70.11 versus 64.41, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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
Gemini 3.6 Flash
Gemini 3.6 Flash leads on the public coding lane, 58.9 to 56.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 3.6 Flash
Gemini 3.6 Flash 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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is 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.
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.
3 Gemini 3.6 Flash only
5 Shared
21 GLM-5.1 only
- Shared results
- 5
- Gemini 3.6 Flash only
- 3
- GLM-5.1 only
- 21
- 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 3.6 Flash
- 68.6
- Supported · #18/181
- GLM-5.1
- 55.4
- Supported · #58/181
- Basis
- BenchAlign lane · 2 vs 4 public rows
- Reading
- Gemini 3.6 Flash leads · intervals overlap
Agentic
Directional only- Gemini 3.6 Flash
- 50.7
- Supported · #60/151
- GLM-5.1
- 50.1
- Estimated · #63/151
- Basis
- BenchAlign lane · 2 vs 9 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.6 Flash
- 77.8
- Unranked · 2 rankable rows
- GLM-5.1
- 69.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.6 Flash
- Not ranked
- GLM-5.1
- 64.1
- #3/7
- Basis
- Provisional lane · 0 vs 4 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.6 Flash
- Not ranked
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.6 Flash
- 82.3
- Unranked · 1 rankable row
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.6 Flash
- Not ranked
- GLM-5.1
- 93.5
- #5/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.6 Flash | GLM-5.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 58.9Supported · #30/183 | 56.8Supported · #39/183 | Like-for-likeBenchAlign lane · 4 vs 7 public rows | Gemini 3.6 Flash leads · intervals overlap |
| Knowledge | 68.6Supported · #18/181 | 55.4Supported · #58/181 | Like-for-likeBenchAlign lane · 2 vs 4 public rows | Gemini 3.6 Flash leads · intervals overlap |
| Agentic | 50.7Supported · #60/151 | 50.1Estimated · #63/151 | Directional onlyBenchAlign lane · 2 vs 9 public rows | Directional only |
| Reasoning | 77.8Unranked · 2 rankable rows | 69.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 64.1#3/7 | Not comparableProvisional lane · 0 vs 4 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 82.3Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.5#5/120 | 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.
LiveCodeBench (Vals)
Coding
MMLU-Pro (Vals)
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
- Gemini 3.6 Flash
- $0.00525
- Fits in one request
- GLM-5.1
- $0.0036
- 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
- Gemini 3.6 Flash
- $0.0975
- Fits in one request
- GLM-5.1
- $0.0832
- 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
- Gemini 3.6 Flash
- $0.135
- Fits in one request
- GLM-5.1
- $0.352
- Does not fit 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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.6 FlashGLM-5.1
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.6 Flash
GLM-5.1
203K
API model ID
Gemini 3.6 Flash
gemini-3.6-flash
Google Gemini 3.6 Flash model documentationGLM-5.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.6 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingGLM-5.1
Not published
Documented inputs
Gemini 3.6 Flash
text, image, video, audio, pdf
Google Gemini 3.6 Flash model documentationGLM-5.1
Not sourced
Documented outputs
Gemini 3.6 Flash
GLM-5.1
Not sourced
Provider availability
Gemini 3.6 Flash
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideGLM-5.1
Not sourced
Reasoning profile
Gemini 3.6 Flash
Reasoning
GLM-5.1
Reasoning
Weight access
Gemini 3.6 Flash
Proprietary
GLM-5.1
Open Weight
License
Gemini 3.6 Flash
Proprietary
GLM-5.1
Open Weight
Release date
Gemini 3.6 Flash
2026-07-21
GLM-5.1
2026-04-07
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 3.6 Flash has the higher public score estimate, 70.11 versus 64.41, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0975 vs $0.0832. Cache-heavy agent loop: $0.135 vs $0.352.
- Context tradeoff
- Gemini 3.6 Flash has the larger documented window (1M).
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.
Gemini 3.6 Flash
API / mo$6,750
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence29 rows
Agentic
OSWorld-Verified
Gemini 3.6 Flash83%SourceGLM-5.1—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.6 Flash73.8%SourceGLM-5.156.9%SourceGemini 3.6 Flash leads this result
Terminal-Bench 2.0
Gemini 3.6 Flash—GLM-5.163.5%SourceNot directly comparable
BrowseComp
Gemini 3.6 Flash—GLM-5.168%SourceNot directly comparable
τ³-bench results
Gemini 3.6 Flash—GLM-5.170.6%SourceNot directly comparable
MCP Atlas
Gemini 3.6 Flash—GLM-5.171.8%SourceNot directly comparable
CyberGym
Gemini 3.6 Flash—GLM-5.168.7%SourceNot directly comparable
Claw-Eval
Gemini 3.6 Flash—GLM-5.162.3%SourceNot directly comparable
Gert Labs
Gemini 3.6 Flash—GLM-5.160.11%SourceNot directly comparable
ResearchClawBench
Gemini 3.6 Flash—GLM-5.118.2%SourceNot directly comparable
Coding
deepSwe
Gemini 3.6 Flash49%SourceGLM-5.1—Not directly comparable
cursorBench32
Gemini 3.6 Flash53.5%SourceGLM-5.1—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.6 Flash88.1%SourceGLM-5.181.4%SourceGemini 3.6 Flash leads this result
SWE-bench (Vals)
Gemini 3.6 Flash79.6%SourceGLM-5.176.4%SourceGemini 3.6 Flash leads this result
SWE-bench Pro
Gemini 3.6 Flash—GLM-5.158.4%SourceNot directly comparable
NL2Repo
Gemini 3.6 Flash—GLM-5.142.7%SourceNot directly comparable
SWE-Rebench
Gemini 3.6 Flash—GLM-5.162.7%SourceNot directly comparable
Vibe Code Bench
Gemini 3.6 Flash—GLM-5.131.46%SourceNot directly comparable
OpenHarmony Bench
Gemini 3.6 Flash—GLM-5.152.3%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Gemini 3.6 Flash93.4%SourceGLM-5.184.5%SourceGemini 3.6 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.6 Flash89.3%SourceGLM-5.186.9%SourceGemini 3.6 Flash leads this result
GPQA-D
Gemini 3.6 Flash—GLM-5.186.2%SourceNot directly comparable
HLE
Gemini 3.6 Flash—GLM-5.152.3%SourceNot directly comparable
Math
AIME26
Gemini 3.6 Flash—GLM-5.195.3%SourceNot directly comparable
HMMT Nov 2025
Gemini 3.6 Flash—GLM-5.194.0%SourceNot directly comparable
HMMT Feb 2026
Gemini 3.6 Flash—GLM-5.182.6%SourceNot directly comparable
MMAnswerBench
Gemini 3.6 Flash—GLM-5.183.8%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
Gemini 3.6 Flash—GLM-5.133.448%SourceNot directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.6 Flash—GLM-5.112.500%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.6 Flash or GLM-5.1?
Gemini 3.6 Flash has the higher public score estimate, 70.11 versus 64.41, 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 3.6 Flash or GLM-5.1?
Gemini 3.6 Flash leads the public coding lane, 58.9 to 56.8, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 3.6 Flash or GLM-5.1?
Gemini 3.6 Flash scores higher for agentic tasks on the public lane, 50.7 to 50.1. GLM-5.1 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 3.6 Flash or GLM-5.1?
For the stated presets, chat costs $0.00525 on Gemini 3.6 Flash and $0.0036 on GLM-5.1; repository review costs $0.0975 and $0.0832; the cache-heavy agent loop costs $0.135 and $0.352. 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, Gemini 3.6 Flash or GLM-5.1?
Gemini 3.6 Flash has the larger documented context window: 1M, compared with 203K.
Related comparisons
Last updated September 4, 2026
Watch Gemini 3.6 Flash vs GLM-5.1
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.