Agentic
Directional only- GPT-5.4 nano
- 42.9
- Holo3-122B-A10B
- 78.9
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
GPT-5.4 nano vs Holo3-122B-A10B
Updated August 13, 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.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
GPT-5.4 nano
GPT-5.4 nano has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano 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
GPT-5.4 nano
GPT-5.4 nano 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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. Holo3-122B-A10B does not fit this workload in one request. Holo3-122B-A10B 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.
12 GPT-5.4 nano only
1 Shared
- Shared results
- 1
- GPT-5.4 nano only
- 12
- Holo3-122B-A10B only
- 0
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- GPT-5.4 nano
- Not measured
- Holo3-122B-A10B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4 nano
- Not measured
- Holo3-122B-A10B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.4 nano
- 43.8
- Holo3-122B-A10B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 21.0
- Holo3-122B-A10B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- Not measured
- Holo3-122B-A10B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 nano
- 66.1
- Holo3-122B-A10B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4 nano
- Not measured
- Holo3-122B-A10B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | Holo3-122B-A10B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 42.9 | 78.9 | Directional only2 vs 1 rows | Directional only |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 43.8 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Math | 21.0 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 66.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
OSWorld-Verified
Agentic
GPT-5.4 nano: 39%Holo3-122B-A10B: 78.8%Normalized gap 39.8GPT-5.4 nano source Holo3-122B-A10B source
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
- GPT-5.4 nano
- $0.00082
- Fits in one request
- Holo3-122B-A10B
- $0.0019
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.4 nano
- $0.01375
- Fits in one request
- Holo3-122B-A10B
- $0.029
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.4 nano
- $0.0205
- Fits in one request
- Holo3-122B-A10B
- $0.118
- Does not fit in one request
- Cached input priced at the published list-input rate
Holo3-122B-A10B does not fit this workload in one request. Holo3-122B-A10B 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.
SpecificationGPT-5.4 nanoHolo3-122B-A10B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
Holo3-122B-A10B
64K
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationHolo3-122B-A10B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingHolo3-122B-A10B
Not published
Documented inputs
GPT-5.4 nano
text, image
OpenAI model catalogHolo3-122B-A10B
Not sourced
Documented outputs
GPT-5.4 nano
Holo3-122B-A10B
Not sourced
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogHolo3-122B-A10B
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
Holo3-122B-A10B
Non-Reasoning
Weight access
GPT-5.4 nano
Proprietary
Holo3-122B-A10B
Proprietary
License
GPT-5.4 nano
Proprietary
Holo3-122B-A10B
Proprietary
Release date
GPT-5.4 nano
2026-03-17
Holo3-122B-A10B
2026-03-31
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: $0.01375 vs $0.029. Cache-heavy agent loop: $0.0205 vs $0.118.
- Context tradeoff
- GPT-5.4 nano has the larger documented window (400K).
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 evidence13 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceHolo3-122B-A10B—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%SourceHolo3-122B-A10B78.8%SourceHolo3-122B-A10B leads this result
MCP Atlas
GPT-5.4 nano56.1%SourceHolo3-122B-A10B—Not directly comparable
Toolathlon
GPT-5.4 nano35.5%SourceHolo3-122B-A10B—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%SourceHolo3-122B-A10B—Not directly comparable
Coding
Vibe Code Bench
GPT-5.4 nano26.10%SourceHolo3-122B-A10B—Not directly comparable
Knowledge
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.4 nano or Holo3-122B-A10B?
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, GPT-5.4 nano or Holo3-122B-A10B?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.4 nano or Holo3-122B-A10B?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.4 nano or Holo3-122B-A10B?
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0019 on Holo3-122B-A10B; repository review costs $0.01375 and $0.029; the cache-heavy agent loop costs $0.0205 and $0.118. Holo3-122B-A10B does not fit this workload in one request. Holo3-122B-A10B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.4 nano or Holo3-122B-A10B?
GPT-5.4 nano has the larger documented context window: 400K, compared with 64K.
Related comparisons
Last updated August 13, 2026
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