Agentic
Like-for-like- GLM-5.2
- 58.5
- Supported · #29/153
- Nemotron 3 Ultra
- 27.0
- Supported · #144/153
- Basis
- BenchAlign lane · 6 vs 6 public rows
- Reading
- GLM-5.2 leads
GLM-5.2 vs Nemotron 3 Ultra
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5.2 has the higher public score, 68.19 versus 40.39, and the 90% score intervals do not overlap.
12 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
GLM-5.2
GLM-5.2 leads on the public coding lane, 61 to 26.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the public agentic lane, 58.5 to 27, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Show secondary and unsupported calls
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
13 GLM-5.2 only
12 Shared
12 Nemotron 3 Ultra only
- Shared results
- 12
- GLM-5.2 only
- 13
- Nemotron 3 Ultra only
- 12
- Like-for-like categories
- 2 / 8
2 categories rest 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.
Coding
Like-for-like- GLM-5.2
- 61.0
- Supported · #19/152
- Nemotron 3 Ultra
- 26.6
- Supported · #146/152
- Basis
- BenchAlign lane · 8 vs 7 public rows
- Reading
- GLM-5.2 leads
Knowledge
Directional only- GLM-5.2
- 60.7
- Supported · #35/183
- Nemotron 3 Ultra
- 44.7
- Estimated · #111/183
- Basis
- BenchAlign lane · 6 vs 7 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5.2
- 89.8
- #22/123
- Nemotron 3 Ultra
- 88.8
- #28/123
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- Nemotron 3 Ultra
- 48.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- Nemotron 3 Ultra
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not ranked
- Nemotron 3 Ultra
- 47.4
- #7/12
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not ranked
- Nemotron 3 Ultra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.2 | Nemotron 3 Ultra | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.5Supported · #29/153 | 27.0Supported · #144/153 | Like-for-likeBenchAlign lane · 6 vs 6 public rows | GLM-5.2 leads |
| Coding | 61.0Supported · #19/152 | 26.6Supported · #146/152 | Like-for-likeBenchAlign lane · 8 vs 7 public rows | GLM-5.2 leads |
| Knowledge | 60.7Supported · #35/183 | 44.7Estimated · #111/183 | Directional onlyBenchAlign lane · 6 vs 7 public rows | Directional only |
| Instruction following | 89.8#22/123 | 88.8#28/123 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 74.8Unranked · 2 rankable rows | 48.7Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 80.7Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 47.4#7/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 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.
HLE
Knowledge
Terminal-Bench 2.0
Agentic
LiveCodeBench (Vals)
Coding
HLE w/o tools
Knowledge
GPQA
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
- GLM-5.2
- $0.0036
- Fits in one request
- Nemotron 3 Ultra
- Self-hosted; infrastructure cost varies
- Fits in one request
Nemotron 3 Ultra has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.2
- $0.0832
- Fits in one request
- Nemotron 3 Ultra
- Self-hosted; infrastructure cost varies
- Fits in one request
Nemotron 3 Ultra has no comparable published API token rate.
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
- Nemotron 3 Ultra
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Nemotron 3 Ultra has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.2Nemotron 3 Ultra
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Nemotron 3 Ultra
1M
API model ID
GLM-5.2
Not sourced
Nemotron 3 Ultra
Not sourced
Cached-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
Nemotron 3 Ultra
No comparable hosted API rate
Documented inputs
GLM-5.2
Not sourced
Nemotron 3 Ultra
Not sourced
Documented outputs
GLM-5.2
Not sourced
Nemotron 3 Ultra
Not sourced
Provider availability
GLM-5.2
Not sourced
Nemotron 3 Ultra
Not sourced
Reasoning profile
GLM-5.2
Reasoning
Nemotron 3 Ultra
Reasoning
Weight access
GLM-5.2
Open Weight
Nemotron 3 Ultra
Open Weight
License
GLM-5.2
Open Weight
Nemotron 3 Ultra
Open Weight
Release date
GLM-5.2
2026-06-16
Nemotron 3 Ultra
2026-06-04
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 score, 68.19 versus 40.39, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
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 evidence37 rows
Agentic
Terminal-Bench 3.0
GLM-5.24.6%SourceNemotron 3 Ultra—Not directly comparable
Terminal-Bench 2.0
GLM-5.281%SourceNemotron 3 Ultra56.4%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceNemotron 3 Ultra—Not directly comparable
Toolathlon
GLM-5.248.2%SourceNemotron 3 Ultra—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceNemotron 3 Ultra—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%SourceNemotron 3 Ultra50.9%SourceGLM-5.2 leads this result
PinchBench
GLM-5.2—Nemotron 3 Ultra90.0%SourceNot directly comparable
BrowseComp
GLM-5.2—Nemotron 3 Ultra44.4%SourceNot directly comparable
τ³-bench results
GLM-5.2—Nemotron 3 Ultra70.9%SourceNot directly comparable
HLE w/ tools
GLM-5.2—Nemotron 3 Ultra37.4%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceNemotron 3 Ultra—Not directly comparable
NL2Repo
GLM-5.248.9%SourceNemotron 3 Ultra—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceNemotron 3 Ultra56.4%SourceGLM-5.2 leads this result
ProgramBench
GLM-5.263.7%SourceNemotron 3 Ultra—Not directly comparable
cursorBench32
GLM-5.255.0%SourceNemotron 3 Ultra—Not directly comparable
OpenHarmony Bench
GLM-5.258.4%SourceNemotron 3 Ultra—Not directly comparable
LiveCodeBench (Vals)
GLM-5.269.5%SourceNemotron 3 Ultra86.0%SourceNemotron 3 Ultra leads this result
SWE-bench (Vals)
GLM-5.282.8%SourceNemotron 3 Ultra69.0%SourceGLM-5.2 leads this result
SWE-bench Verified
GLM-5.2—Nemotron 3 Ultra71.9%SourceNot directly comparable
SWE Multilingual
GLM-5.2—Nemotron 3 Ultra67.7%SourceNot directly comparable
LiveCodeBench v6
GLM-5.2—Nemotron 3 Ultra89.0%SourceNot directly comparable
SciCode
GLM-5.2—Nemotron 3 Ultra44.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-5.291.2%SourceNemotron 3 Ultra87%SourceGLM-5.2 leads this result
GPQA-D
GLM-5.291.2%SourceNemotron 3 Ultra87.0%SourceGLM-5.2 leads this result
HLE
GLM-5.254.7%SourceNemotron 3 Ultra26.7%SourceGLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%SourceNemotron 3 Ultra26.7%SourceGLM-5.2 leads this result
GPQA Diamond (Vals)
GLM-5.285.6%SourceNemotron 3 Ultra86.1%SourceNemotron 3 Ultra leads this result
MMLU-Pro (Vals)
GLM-5.286.7%SourceNemotron 3 Ultra85.8%SourceGLM-5.2 leads this result
MMLU-Pro
GLM-5.2—Nemotron 3 Ultra86.8%SourceNot directly comparable
Math
Multilingual
MMLU-ProX
GLM-5.2—Nemotron 3 Ultra83%SourceNot directly comparable
Instruction following
IFBench
GLM-5.2—Nemotron 3 Ultra81.7%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.2 or Nemotron 3 Ultra?
GLM-5.2 has the higher public score, 68.19 versus 40.39, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GLM-5.2 or Nemotron 3 Ultra?
GLM-5.2 leads the public coding lane, 61 to 26.6, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GLM-5.2 or Nemotron 3 Ultra?
GLM-5.2 leads the public agentic tasks lane, 58.5 to 27, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GLM-5.2 or Nemotron 3 Ultra?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GLM-5.2 or Nemotron 3 Ultra?
Both models list the same context window, 1M.
Related comparisons
Last updated September 14, 2026
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