Long documents
Prompts that approach the documented context limit
Particle-1
Particle-1 has the larger documented context window.
Confidence: documented
GLM-5 vs Particle-1
Updated October 29, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
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. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- GLM-5 only
- 37
- Particle-1 only
- 4
- Like-for-like categories
- 0 / 8
Supported: GLM-5How 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.
Chat turn cost
1K fresh input + 500 output tokens
Particle-1
Particle-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
Particle-1
Particle-1 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
Particle-1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Particle-1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-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 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
38.6GLM-5—Particle-1
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
37 GLM-5 only
1 Shared
4 Particle-1 only
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
Not comparable- GLM-5
- 43.1
- Estimated · #69/135
- Particle-1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 13 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- GLM-5
- 38.6
- Estimated · #73/156
- Particle-1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 8 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5
- 70.1
- Unranked · 4 rankable rows
- Particle-1
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5
- Not ranked
- Particle-1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5
- 49.3
- Estimated · #81/179
- Particle-1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 6 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5
- 89.5
- #6/17
- Particle-1
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5
- 88.6
- #38/157
- Particle-1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5
- 83.0
- Unranked · 6 rankable rows
- Particle-1
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5 | Particle-1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.1Estimated · #69/135 | Not ranked | Not comparableBenchAlign v5.8 lane · 13 vs 1 public rows | Not comparable |
| Coding | 38.6Estimated · #73/156 | Not ranked | Not comparableBenchAlign v5.8 lane · 8 vs 2 public rows | Not comparable |
| Reasoning | 70.1Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 49.3Estimated · #81/179 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 2 public rows | Not comparable |
| Multilingual | 89.5#6/17 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | 88.6#38/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 83.0Unranked · 6 rankable rows | Not ranked | Not comparableProvisional lane · 2 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.
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
- $0.0026
- Fits in one request
- Particle-1
- $0.00075
- Fits in one request
Particle-1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5
- $0.0596
- Fits in one request
- Particle-1
- $0.0155
- Fits in one request
Particle-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
- $0.252
- Does not fit in one request
- Cached input priced at the published list-input rate
- Particle-1
- $0.021
- Fits in one request
GLM-5 does not fit this workload in one request. GLM-5 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-5Particle-1
Context window
Maximum documented context; output-token limits may be lower.
GLM-5
200K
Particle-1
API model ID
GLM-5
Not sourced
Particle-1
particle-1
Particle API model catalogCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5
Not published
Particle-1
$0.03 per 1M cached input tokens
Particle API usage and billingDocumented inputs
GLM-5
Not sourced
Particle-1
text, image
Particle API model catalogDocumented outputs
GLM-5
Not sourced
Particle-1
Provider availability
GLM-5
Not sourced
Particle-1
Generally Available · Particle API
Particle AI: Introducing Particle-1Reasoning profile
GLM-5
Non-Reasoning
Particle-1
Reasoning
Weight access
GLM-5
Open Weight
Particle-1
Proprietary
License
GLM-5
Open Weight
Particle-1
Proprietary
Release date
GLM-5
2026-02-12
Particle-1
2026-10-28
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: $0.0596 vs $0.0155. Cache-heavy agent loop: $0.252 vs $0.021.
- Context tradeoff
- Particle-1 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5 or Particle-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 or Particle-1?
Particle-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 or Particle-1?
Particle-1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5 or Particle-1?
For the stated presets, chat costs $0.0026 on GLM-5 and $0.00075 on Particle-1; repository review costs $0.0596 and $0.0155; the cache-heavy agent loop costs $0.252 and $0.021. GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5 or Particle-1?
Particle-1 has the larger documented context window: 1M, compared with 200K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GLM-5
API / mo$3,150
Self-host / mo$27,962
Break-even561M/day
Particle-1
API / mo$938
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 evidence42 rows
Agentic
Terminal-Bench 2.0
GLM-556.2%SourceParticle-1—Not directly comparable
BrowseComp
GLM-562%SourceParticle-1—Not directly comparable
HLE w/ tools
GLM-550.4%SourceParticle-1—Not directly comparable
Claw-Eval
GLM-557.7%SourceParticle-1—Not directly comparable
QwenClawBench
GLM-554.1%SourceParticle-1—Not directly comparable
τ³-bench results
GLM-565.6%SourceParticle-1—Not directly comparable
DeepPlanning
GLM-514.6%SourceParticle-1—Not directly comparable
Toolathlon
GLM-538%SourceParticle-1—Not directly comparable
MCP Atlas
GLM-531.1%SourceParticle-1—Not directly comparable
MCP-Tasks
GLM-560.8%SourceParticle-1—Not directly comparable
WideResearch
GLM-569.8%SourceParticle-1—Not directly comparable
CyberGym
GLM-543.2%SourceParticle-1—Not directly comparable
Gert Labs
GLM-550.99%SourceParticle-1—Not directly comparable
Terminal-Bench 2.1
GLM-5—Particle-179.9%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-577.8%SourceParticle-1—Not directly comparable
SWE-bench Verified*
GLM-572.8%SourceParticle-1—Not directly comparable
SWE-bench Pro
GLM-555.1%SourceParticle-1—Not directly comparable
SWE Multilingual
GLM-573.3%SourceParticle-1—Not directly comparable
NL2Repo
GLM-535.9%SourceParticle-1—Not directly comparable
SWE-Rebench
GLM-562.8%SourceParticle-1—Not directly comparable
React Native Evals
GLM-574.8%SourceParticle-1—Not directly comparable
Terminal-Bench 2.0
GLM-556.2%SourceParticle-1—Not directly comparable
Terminal-Bench 2.1
GLM-5—Particle-179.9%SourceNot directly comparable
DeepSWE
GLM-5—Particle-172.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-586%SourceParticle-1—Not directly comparable
GPQA-D
GLM-586.0%SourceParticle-190.4%SourceParticle-1 leads this result
SuperGPQA
GLM-566.8%SourceParticle-1—Not directly comparable
MMLU-Pro
GLM-585.7%SourceParticle-1—Not directly comparable
MMLU-Pro (Arcee)
GLM-585.8%SourceParticle-1—Not directly comparable
HLE
GLM-530.5%SourceParticle-1—Not directly comparable
MMLU
GLM-5—Particle-186.1%SourceNot directly comparable
Multilingual
Instruction following
IFEval
GLM-592.6%SourceParticle-1—Not directly comparable
Math
AIME26
GLM-595.8%SourceParticle-1—Not directly comparable
AIME25 (Arcee)
GLM-593.3%SourceParticle-1—Not directly comparable
HMMT Feb 2025
GLM-597.5%SourceParticle-1—Not directly comparable
HMMT Nov 2025
GLM-596.9%SourceParticle-1—Not directly comparable
HMMT Feb 2026
GLM-586.4%SourceParticle-1—Not directly comparable
MMAnswerBench
GLM-582.5%SourceParticle-1—Not directly comparable
42 public results · 1 shared
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Last updated October 29, 2026