Chat turn cost
1K fresh input + 500 output tokens
Quasar 438B
Quasar 438B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
GLM-5.2 vs Quasar 438B
Updated October 17, 2026. Rank says GLM-5.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
GLM-5.2 has the higher public point estimate, 60.5 versus 48.46. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model B
Multiverse Computing
48.46/100
Estimated · Public rank #102
Conditional range 34.1–62.8
- Shared results
- 2
- GLM-5.2 only
- 28
- Quasar 438B only
- 0
- Like-for-like categories
- 0 / 8
Supported: GLM-5.2 · Estimated: Quasar 438B. Conditional ranges do not establish rank confidence.How 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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Quasar 438B
Quasar 438B 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. Quasar 438B 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
Quasar 438B
Quasar 438B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Quasar 438B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Quasar 438B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
53.7GLM-5.241.2Quasar 438B
Directional only · BenchAlign v5.8
GLM-5.2 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
28 GLM-5.2 only
2 Shared
3 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
Agentic
Directional only- GLM-5.2
- 57.4
- Supported · #35/128
- Quasar 438B
- 40.0
- Estimated · #67/128
- Basis
- BenchAlign v5.8 lane · 8 vs 1 public rows
- Reading
- Directional only
Coding
Directional only- GLM-5.2
- 53.7
- Supported · #33/150
- Quasar 438B
- 41.2
- Estimated · #63/150
- Basis
- BenchAlign v5.8 lane · 9 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5.2
- 57.9
- Supported · #53/184
- Quasar 438B
- 46.7
- Estimated · #92/184
- Basis
- BenchAlign v5.8 lane · 6 vs 0 public rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- 79.6
- Unranked · 2 rankable rows
- Quasar 438B
- 78.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not ranked
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not ranked
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- 89.9
- #26/148
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 78.4
- #3/12
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.2 | Quasar 438B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 57.4Supported · #35/128 | 40.0Estimated · #67/128 | Directional onlyBenchAlign v5.8 lane · 8 vs 1 public rows | Directional only |
| Coding | 53.7Supported · #33/150 | 41.2Estimated · #63/150 | Directional onlyBenchAlign v5.8 lane · 9 vs 1 public rows | Directional only |
| Knowledge | 57.9Supported · #53/184 | 46.7Estimated · #92/184 | Directional onlyBenchAlign v5.8 lane · 6 vs 0 public rows | Directional only |
| Reasoning | 79.6Unranked · 2 rankable rows | 78.3Unranked · 2 rankable rows | 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 |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 89.9#26/148 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 78.4#3/12 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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-5.2
- $0.0036
- Fits in one request
- Quasar 438B
- $0.0015
- Fits in one request
Quasar 438B has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5.2
- $0.0832
- Fits in one request
- Quasar 438B
- $0.0354
- Fits in one request
Quasar 438B 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-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
- Quasar 438B
- $0.15
- Fits in one request
- Cached input priced at the published list-input rate
Quasar 438B has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Quasar 438B has no published cached-input rate, so cached tokens use its listed input 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-5.2Quasar 438B
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Quasar 438B
API model ID
GLM-5.2
Not sourced
Quasar 438B
quasar-438b
CompactifAI Quasar 438B documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.2
Not published
Quasar 438B
Not published
CompactifAI API pricingDocumented inputs
GLM-5.2
Not sourced
Quasar 438B
Not sourced
Documented outputs
GLM-5.2
Not sourced
Quasar 438B
Not sourced
Provider availability
GLM-5.2
Not sourced
Quasar 438B
Generally Available · CompactifAI API
CompactifAI Quasar 438B documentationReasoning profile
GLM-5.2
Reasoning
Quasar 438B
Reasoning
Weight access
GLM-5.2
Open Weight
Quasar 438B
Proprietary
License
GLM-5.2
Open Weight
Quasar 438B
Proprietary
Release date
GLM-5.2
2026-06-16
Quasar 438B
2026-09-02
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
- GLM-5.2 has the higher public point estimate, 60.5 versus 48.46. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.0832 vs $0.0354. Cache-heavy agent loop: $0.352 vs $0.15.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5.2 or Quasar 438B?
GLM-5.2 has the higher public point estimate, 60.5 versus 48.46. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5.2 or Quasar 438B?
GLM-5.2 scores higher for coding on the public lane, 53.7 to 41.2. Quasar 438B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5.2 or Quasar 438B?
GLM-5.2 scores higher for agentic tasks on the public lane, 57.4 to 40. Quasar 438B 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-5.2 or Quasar 438B?
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.0015 on Quasar 438B; repository review costs $0.0832 and $0.0354; the cache-heavy agent loop costs $0.352 and $0.15. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5.2 or Quasar 438B?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence30 rows
Agentic
Terminal-Bench 3.0
GLM-5.24.6%SourceQuasar 438B—Not directly comparable
Terminal-Bench 2.1
GLM-5.281.0%SourceQuasar 438B69.3%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceQuasar 438B—Not directly comparable
Toolathlon
GLM-5.248.2%SourceQuasar 438B—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceQuasar 438B—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%SourceQuasar 438B—Not directly comparable
HLE w/ tools
GLM-5.254.7%SourceQuasar 438B—Not directly comparable
MazeBench
GLM-5.20%SourceQuasar 438B—Not directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceQuasar 438B—Not directly comparable
NL2Repo
GLM-5.248.9%SourceQuasar 438B—Not directly comparable
Terminal-Bench 2.1
GLM-5.281.0%SourceQuasar 438B69.3%SourceGLM-5.2 leads this result
ProgramBench
GLM-5.263.7%SourceQuasar 438B—Not directly comparable
CursorBench 3.2
GLM-5.255.0%SourceQuasar 438B—Not directly comparable
OpenHarmony Bench
GLM-5.258.4%SourceQuasar 438B—Not directly comparable
LiveCodeBench (Vals)
GLM-5.269.5%SourceQuasar 438B—Not directly comparable
SWE-bench (Vals)
GLM-5.282.8%SourceQuasar 438B—Not directly comparable
PostTrainBench v1.1
GLM-5.231.7%SourceQuasar 438B—Not directly comparable
Reasoning
CritPt
GLM-5.220.9%SourceQuasar 438B—Not directly comparable
Knowledge
GPQA
GLM-5.291.2%SourceQuasar 438B—Not directly comparable
GPQA-D
GLM-5.291.2%SourceQuasar 438B—Not directly comparable
HLE
GLM-5.240.5%SourceQuasar 438B—Not directly comparable
HLE w/o tools
GLM-5.240.5%SourceQuasar 438B—Not directly comparable
GPQA Diamond (Vals)
GLM-5.285.6%SourceQuasar 438B—Not directly comparable
MMLU-Pro (Vals)
GLM-5.286.7%SourceQuasar 438B—Not directly comparable
Math
AIME26
GLM-5.299.2%SourceQuasar 438B—Not directly comparable
HMMT Nov 2025
GLM-5.294.4%SourceQuasar 438B—Not directly comparable
HMMT Feb 2026
GLM-5.292.5%SourceQuasar 438B—Not directly comparable
MMAnswerBench
GLM-5.291.0%SourceQuasar 438B—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.259.206%SourceQuasar 438B—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.229.268%SourceQuasar 438B—Not directly comparable
30 public results · 2 shared
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Last updated October 17, 2026