Agentic
Not comparable- GLM-5
- 51.0
- Estimated · #45/152
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 11 vs 4 public rows
- Reading
- Not comparable
GLM-5 vs Solar Pro 4
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.
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.
Long documents
Prompts that approach the documented context limit
Solar Pro 4
Solar Pro 4 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Solar Pro 4
Solar Pro 4 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
Solar Pro 4
Solar Pro 4 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
Solar Pro 4 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
Solar Pro 4 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 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
31 GLM-5 only
5 Shared
5 Solar Pro 4 only
- Shared results
- 5
- GLM-5 only
- 31
- Solar Pro 4 only
- 5
- 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
- 56.1
- Estimated · #39/151
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 6 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5
- 52.1
- Unranked · 4 rankable rows
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5
- 54.1
- Estimated · #57/183
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- GLM-5
- 56.9
- #7/7
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 4 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5
- 48.7
- #6/12
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5
- 88.5
- #31/123
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5 | Solar Pro 4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 51.0Estimated · #45/152 | Not ranked | Not comparableBenchAlign lane · 11 vs 4 public rows | Not comparable |
| Coding | 56.1Estimated · #39/151 | Not ranked | Not comparableBenchAlign lane · 6 vs 3 public rows | Not comparable |
| Reasoning | 52.1Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 54.1Estimated · #57/183 | Not ranked | Not comparableBenchAlign lane · 6 vs 2 public rows | Not comparable |
| Math | 56.9#7/7 | Not ranked | Not comparableProvisional lane · 4 vs 1 weighted rows | Not comparable |
| Multilingual | 48.7#6/12 | 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 |
| Instruction following | 88.5#31/123 | 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.
SWE-bench Verified
Coding
MMLU-Pro
Knowledge
AIME26
Math
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
- Solar Pro 4
- $0.0009
- Fits in one request
Solar Pro 4 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
- Solar Pro 4
- $0.0186
- Fits in one request
Solar Pro 4 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
- Solar Pro 4
- $0.03
- 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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5Solar Pro 4
Context window
Maximum documented context; output-token limits may be lower.
GLM-5
200K
Solar Pro 4
API model ID
GLM-5
Not sourced
Solar Pro 4
solar-pro4
Upstage Solar Pro 4 launch postCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5
Not published
Solar Pro 4
$0.06 per 1M cached input tokens
Upstage Solar Pro 4 launch postDocumented inputs
GLM-5
Not sourced
Solar Pro 4
Not sourced
Documented outputs
GLM-5
Not sourced
Solar Pro 4
Not sourced
Provider availability
GLM-5
Not sourced
Solar Pro 4
Not sourced
Reasoning profile
GLM-5
Non-Reasoning
Solar Pro 4
Reasoning
Weight access
GLM-5
Open Weight
Solar Pro 4
Proprietary
License
GLM-5
Open Weight
Solar Pro 4
Proprietary
Release date
GLM-5
2026-03-01
Solar Pro 4
2026-08-11
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 $0.0186. Cache-heavy agent loop: $0.252 vs $0.03.
- Context tradeoff
- Solar Pro 4 has the larger documented window (512K).
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 evidence41 rows
Agentic
Terminal-Bench 2.0
GLM-556.2%SourceSolar Pro 4—Not directly comparable
Claw-Eval
GLM-557.7%SourceSolar Pro 4—Not directly comparable
QwenClawBench
GLM-554.1%SourceSolar Pro 4—Not directly comparable
τ³-bench results
GLM-565.6%SourceSolar Pro 4—Not directly comparable
DeepPlanning
GLM-514.6%SourceSolar Pro 4—Not directly comparable
Toolathlon
GLM-538%SourceSolar Pro 4—Not directly comparable
MCP Atlas
GLM-531.1%SourceSolar Pro 461.4%SourceSolar Pro 4 leads this result
MCP-Tasks
GLM-560.8%SourceSolar Pro 4—Not directly comparable
WideResearch
GLM-569.8%SourceSolar Pro 4—Not directly comparable
CyberGym
GLM-543.2%SourceSolar Pro 4—Not directly comparable
Gert Labs
GLM-550.99%SourceSolar Pro 4—Not directly comparable
Terminal-Bench 2.1
GLM-5—Solar Pro 457.0%SourceNot directly comparable
BrowseComp
GLM-5—Solar Pro 449.2%SourceNot directly comparable
APEX-Agents
GLM-5—Solar Pro 418.7%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-577.8%SourceSolar Pro 470.6%SourceGLM-5 leads this result
SWE-bench Verified*
GLM-572.8%SourceSolar Pro 4—Not directly comparable
SWE-bench Pro
GLM-555.1%SourceSolar Pro 4—Not directly comparable
SWE Multilingual
GLM-573.3%SourceSolar Pro 4—Not directly comparable
SWE-Rebench
GLM-562.8%SourceSolar Pro 4—Not directly comparable
React Native Evals
GLM-574.8%SourceSolar Pro 4—Not directly comparable
Terminal-Bench 2.1
GLM-5—Solar Pro 457.0%SourceNot directly comparable
LiveCodeBench
GLM-5—Solar Pro 487.8%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-586%SourceSolar Pro 4—Not directly comparable
GPQA-D
GLM-586.0%SourceSolar Pro 489.0%SourceSolar Pro 4 leads this result
SuperGPQA
GLM-566.8%SourceSolar Pro 4—Not directly comparable
MMLU-Pro
GLM-585.7%SourceSolar Pro 486.3%SourceSolar Pro 4 leads this result
MMLU-Pro (Arcee)
GLM-585.8%SourceSolar Pro 4—Not directly comparable
HLE
GLM-550.4%SourceSolar Pro 4—Not directly comparable
Math
AIME26
GLM-595.8%SourceSolar Pro 495.3%SourceGLM-5 leads this result
AIME25 (Arcee)
GLM-593.3%SourceSolar Pro 4—Not directly comparable
HMMT Feb 2025
GLM-597.5%SourceSolar Pro 4—Not directly comparable
HMMT Nov 2025
GLM-596.9%SourceSolar Pro 4—Not directly comparable
HMMT Feb 2026
GLM-586.4%SourceSolar Pro 4—Not directly comparable
MMAnswerBench
GLM-582.5%SourceSolar Pro 4—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-516.434%SourceSolar Pro 4—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-52.100%SourceSolar Pro 4—Not directly comparable
Multilingual
Instruction following
IFEval
GLM-592.6%SourceSolar Pro 4—Not directly comparable
Frequently asked questions
Which is better, GLM-5 or Solar Pro 4?
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 Solar Pro 4?
Solar Pro 4 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 Solar Pro 4?
Solar Pro 4 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5 or Solar Pro 4?
For the stated presets, chat costs $0.0026 on GLM-5 and $0.0009 on Solar Pro 4; repository review costs $0.0596 and $0.0186; the cache-heavy agent loop costs $0.252 and $0.03. 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 Solar Pro 4?
Solar Pro 4 has the larger documented context window: 512K, compared with 200K.
Related comparisons
Last updated September 10, 2026
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