Agentic
Not comparable- GLM-5.1
- 54.3
- Estimated · #37/152
- Laguna XS 2.1
- Not ranked
- Basis
- BenchAlign lane · 9 vs 1 public rows
- Reading
- Not comparable
GLM-5.1 vs Laguna XS 2.1
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 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.
Long documents
Prompts that approach the documented context limit
Laguna XS 2.1
Laguna XS 2.1 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Laguna XS 2.1
Laguna XS 2.1 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
Laguna XS 2.1
Laguna XS 2.1 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
Laguna XS 2.1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna XS 2.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
24 GLM-5.1 only
2 Shared
3 Laguna XS 2.1 only
- Shared results
- 2
- GLM-5.1 only
- 24
- Laguna XS 2.1 only
- 3
- Like-for-like categories
- 0 / 8
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.
Coding
Not comparable- GLM-5.1
- 56.4
- Supported · #38/151
- Laguna XS 2.1
- Not ranked
- Basis
- BenchAlign lane · 7 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.1
- 71.7
- Unranked · 2 rankable rows
- Laguna XS 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.1
- 54.8
- Supported · #54/183
- Laguna XS 2.1
- Not ranked
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.1
- 64.1
- #3/7
- Laguna XS 2.1
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not ranked
- Laguna XS 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not ranked
- Laguna XS 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.1
- 93.7
- #5/123
- Laguna XS 2.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.1 | Laguna XS 2.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 54.3Estimated · #37/152 | Not ranked | Not comparableBenchAlign lane · 9 vs 1 public rows | Not comparable |
| Coding | 56.4Supported · #38/151 | Not ranked | Not comparableBenchAlign lane · 7 vs 4 public rows | Not comparable |
| Reasoning | 71.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 54.8Supported · #54/183 | Not ranked | Not comparableBenchAlign lane · 4 vs 0 public rows | Not comparable |
| Math | 64.1#3/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.7#5/123 | Not ranked | 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
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
- Laguna XS 2.1
- $0.0002
- Fits in one request
Laguna XS 2.1 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
- Laguna XS 2.1
- $0.0056
- Fits in one request
Laguna XS 2.1 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
- Laguna XS 2.1
- $0.014
- 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-5.1Laguna XS 2.1
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
Laguna XS 2.1
API model ID
GLM-5.1
Not sourced
Laguna XS 2.1
poolside/Laguna-XS-2.1
Poolside Laguna XS 2.1 launch postCached-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
Laguna XS 2.1
$0.05 per 1M cached input tokens
Poolside Laguna XS 2.1 launch postDocumented inputs
GLM-5.1
Not sourced
Laguna XS 2.1
Not sourced
Documented outputs
GLM-5.1
Not sourced
Laguna XS 2.1
Not sourced
Provider availability
GLM-5.1
Not sourced
Laguna XS 2.1
Not sourced
Reasoning profile
GLM-5.1
Reasoning
Laguna XS 2.1
Reasoning
Weight access
GLM-5.1
Open Weight
Laguna XS 2.1
Open Weight
License
GLM-5.1
Open Weight
Laguna XS 2.1
Open Weight
Release date
GLM-5.1
2026-04-07
Laguna XS 2.1
2026-07-02
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0832 vs $0.0056. Cache-heavy agent loop: $0.352 vs $0.014.
- Context tradeoff
- Laguna XS 2.1 has the larger documented window (262K).
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
Laguna XS 2.1
API / mo$225
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 evidence29 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceLaguna XS 2.137.5%SourceGLM-5.1 leads this result
BrowseComp
GLM-5.168%SourceLaguna XS 2.1—Not directly comparable
τ³-bench results
GLM-5.170.6%SourceLaguna XS 2.1—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceLaguna XS 2.1—Not directly comparable
CyberGym
GLM-5.168.7%SourceLaguna XS 2.1—Not directly comparable
Claw-Eval
GLM-5.162.3%SourceLaguna XS 2.1—Not directly comparable
Gert Labs
GLM-5.160.11%SourceLaguna XS 2.1—Not directly comparable
ResearchClawBench
GLM-5.118.2%SourceLaguna XS 2.1—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%SourceLaguna XS 2.1—Not directly comparable
Coding
SWE-bench Pro
GLM-5.158.4%SourceLaguna XS 2.147.6%SourceGLM-5.1 leads this result
NL2Repo
GLM-5.142.7%SourceLaguna XS 2.1—Not directly comparable
SWE-Rebench
GLM-5.162.7%SourceLaguna XS 2.1—Not directly comparable
Vibe Code Bench
GLM-5.131.46%SourceLaguna XS 2.1—Not directly comparable
OpenHarmony Bench
GLM-5.152.3%SourceLaguna XS 2.1—Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%SourceLaguna XS 2.1—Not directly comparable
SWE-bench (Vals)
GLM-5.176.4%SourceLaguna XS 2.1—Not directly comparable
SWE-bench Verified
GLM-5.1—Laguna XS 2.170.9%SourceNot directly comparable
SWE Multilingual
GLM-5.1—Laguna XS 2.163.1%SourceNot directly comparable
Terminal-Bench 2.0
GLM-5.1—Laguna XS 2.137.5%SourceNot directly comparable
Knowledge
Math
AIME26
GLM-5.195.3%SourceLaguna XS 2.1—Not directly comparable
HMMT Nov 2025
GLM-5.194.0%SourceLaguna XS 2.1—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceLaguna XS 2.1—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceLaguna XS 2.1—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.133.448%SourceLaguna XS 2.1—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.112.500%SourceLaguna XS 2.1—Not directly comparable
Frequently asked questions
Which is better, GLM-5.1 or Laguna XS 2.1?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5.1 or Laguna XS 2.1?
Laguna XS 2.1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GLM-5.1 or Laguna XS 2.1?
Laguna XS 2.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5.1 or Laguna XS 2.1?
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.0002 on Laguna XS 2.1; repository review costs $0.0832 and $0.0056; the cache-heavy agent loop costs $0.352 and $0.014. 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 Laguna XS 2.1?
Laguna XS 2.1 has the larger documented context window: 262K, compared with 203K.
Related comparisons
Last updated September 10, 2026
Watch GLM-5.1 vs Laguna XS 2.1
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.