Knowledge
Directional only- GPT-5.4 nano
- 43.8
- Ling 3.0 Flash FP8
- 84.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Model comparison
GPT-5.4 nano vs Ling 3.0 Flash FP8
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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
Show secondary and unsupported calls
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
No shared weighted benchmark basis supports a winner.
Confidence: limited
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: listed-rates
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.
12 GPT-5.4 nano only
1 Shared
3 Ling 3.0 Flash FP8 only
- Shared results
- 1
- GPT-5.4 nano only
- 12
- Ling 3.0 Flash FP8 only
- 3
- 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.
Agentic
Not comparable- GPT-5.4 nano
- 42.9
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.4 nano
- Not measured
- Ling 3.0 Flash FP8
- 40.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4 nano
- Not measured
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 21.0
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- Not measured
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 nano
- 66.1
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4 nano
- Not measured
- Ling 3.0 Flash FP8
- 73.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | Ling 3.0 Flash FP8 | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 43.8 | 84.0 | Directional only2 vs 1 rows | Directional only |
| Agentic | 42.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | Not measured | 40.4 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 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 | 73.4 | Not comparable0 vs 1 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.
GPQA
Knowledge
GPT-5.4 nano: 82.8%Ling 3.0 Flash FP8: 84%Normalized gap 1.2GPT-5.4 nano source Ling 3.0 Flash FP8 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
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
Ling 3.0 Flash FP8 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.4 nano
- $0.01375
- Fits in one request
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
Ling 3.0 Flash FP8 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.4 nano
- $0.0205
- Fits in one request
- Ling 3.0 Flash FP8
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Ling 3.0 Flash FP8 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.
SpecificationGPT-5.4 nanoLing 3.0 Flash FP8
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
Ling 3.0 Flash FP8
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationLing 3.0 Flash FP8
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 pricingLing 3.0 Flash FP8
No comparable hosted API rate
InclusionAI Ling 3.0 Flash FP8 model cardDocumented inputs
GPT-5.4 nano
text, image
OpenAI model catalogLing 3.0 Flash FP8
Not sourced
Documented outputs
GPT-5.4 nano
Ling 3.0 Flash FP8
Not sourced
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogLing 3.0 Flash FP8
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
Ling 3.0 Flash FP8
Reasoning
Weight access
GPT-5.4 nano
Proprietary
Ling 3.0 Flash FP8
Open Weight
License
GPT-5.4 nano
Proprietary
Ling 3.0 Flash FP8
Open Weight
Release date
GPT-5.4 nano
2026-03-17
Ling 3.0 Flash FP8
2026-08-04
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
- 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
- A complete comparable API-rate estimate is not available for both models.
- 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 evidence16 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceLing 3.0 Flash FP8—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%SourceLing 3.0 Flash FP8—Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%SourceLing 3.0 Flash FP8—Not directly comparable
Toolathlon
GPT-5.4 nano35.5%SourceLing 3.0 Flash FP8—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%SourceLing 3.0 Flash FP8—Not directly comparable
Coding
Knowledge
GPQA
GPT-5.4 nano82.8%SourceLing 3.0 Flash FP884%SourceLing 3.0 Flash FP8 leads this result
HLE
GPT-5.4 nano37.7%SourceLing 3.0 Flash FP8—Not directly comparable
HLE w/o tools
GPT-5.4 nano24.3%SourceLing 3.0 Flash FP8—Not directly comparable
GPQA-D
GPT-5.4 nano—Ling 3.0 Flash FP884.0%SourceNot directly comparable
Math
Multimodal
Instruction following
IFBench
GPT-5.4 nano—Ling 3.0 Flash FP873.4%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.4 nano or Ling 3.0 Flash FP8?
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 Ling 3.0 Flash FP8?
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 Ling 3.0 Flash FP8?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.4 nano or Ling 3.0 Flash FP8?
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, GPT-5.4 nano or Ling 3.0 Flash FP8?
GPT-5.4 nano has the larger documented context window: 400K, compared with 262K.
Related comparisons
Last updated August 4, 2026
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