Long documents
Prompts that approach the documented context limit
Qwen3.5 Flash
Qwen3.5 Flash has the larger documented context window.
Confidence: documented
GPT-5.4 nano vs Qwen3.5 Flash
Updated November 11, 2026. Rank says GPT-5.4 nano is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
GPT-5.4 nano has the higher public point estimate, 46.51 versus 42.31. Their conditional score ranges overlap. These ranges do not establish rank confidence. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- API price per 1M tokens
GPT-5.4 nano: $0.20 input / $1.25 output
Qwen3.5 Flash: $0.10 input / $0.40 output
- Context window
GPT-5.4 nano: 400K
Qwen3.5 Flash: 1M
- Released
GPT-5.4 nano: March 17, 2026
Qwen3.5 Flash: March 4, 2026
- Shared results
- 5
- GPT-5.4 nano only
- 18
- Qwen3.5 Flash only
- 0
- Like-for-like categories
- 0 / 8
Supported: GPT-5.4 nano · Estimated: Qwen3.5 Flash. Conditional ranges do not establish rank confidence.How 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
Qwen3.5 Flash
Qwen3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Qwen3.5 Flash
Qwen3.5 Flash 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
Qwen3.5 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.5 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
30.2GPT-5.4 nano28.4Qwen3.5 Flash
Directional only · BenchAlign v5.8
GPT-5.4 nano has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
18 GPT-5.4 nano only
5 Shared
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 26.7GPT-5.4 nano:44.912%Qwen3.5 Flash:18.246%MMLU-Pro (Vals)Knowledge
Normalized gap 6.9GPT-5.4 nano:77.2%Qwen3.5 Flash:84.1%LiveCodeBench (Vals)Coding
Normalized gap 0.7GPT-5.4 nano:84.0%Qwen3.5 Flash:83.3%
Bars run 0–100 on each benchmark’s normalized display scale
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, reasoning, multimodal, 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
Directional only- GPT-5.4 nano
- 30.2
- Supported · #108/158
- Qwen3.5 Flash
- 28.4
- Estimated · #113/158
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.4 nano
- 38.9
- Supported · #120/179
- Qwen3.5 Flash
- 43.7
- Estimated · #101/179
- Basis
- BenchAlign v5.8 lane · 5 vs 2 public rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.4 nano
- 33.3
- Supported · #96/132
- Qwen3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 6 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4 nano
- 37.6
- Supported · #95/158
- Qwen3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 nano
- 38.8
- Supported · #63/67
- Qwen3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4 nano
- 93.3
- #12/157
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 48.3
- Unranked · 2 rankable rows
- Qwen3.5 Flash
- 32.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | Qwen3.5 Flash | Basis | Reading |
|---|---|---|---|---|
| Coding | 30.2Supported · #108/158 | 28.4Estimated · #113/158 | Directional onlyBenchAlign v5.8 lane · 4 vs 2 public rows | Directional only |
| Knowledge | 38.9Supported · #120/179 | 43.7Estimated · #101/179 | Directional onlyBenchAlign v5.8 lane · 5 vs 2 public rows | Directional only |
| Agentic | 33.3Supported · #96/132 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 0 public rows | Not comparable |
| Reasoning | 37.6Supported · #95/158 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 0 public rows | Not comparable |
| Multimodal | 38.8Supported · #63/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.3#12/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 48.3Unranked · 2 rankable rows | 32.6Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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.
Supported evidence per lane · 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
- GPT-5.4 nano
- $0.00082
- Fits in one request
- Qwen3.5 Flash
- $0.0003
- Fits in one request
Qwen3.5 Flash 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
- Qwen3.5 Flash
- $0.0062
- Fits in one request
Qwen3.5 Flash 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
- Qwen3.5 Flash
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.4 nano has the lower modeled cost
Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Your workload
Enter a month of tokens to price it at both models’ listed API rates.
- GPT-5.4 nano
- $4.50
- per month
- Qwen3.5 Flash
- $1.80
- per month
Standard listed rates, no batch or volume discounts.
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.
SpecificationGPT-5.4 nanoQwen3.5 Flash
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
Qwen3.5 Flash
1M
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationQwen3.5 Flash
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 pricingQwen3.5 Flash
Not published
Documented inputs
GPT-5.4 nano
text, image
OpenAI model catalogQwen3.5 Flash
text, image, video
Qwen Cloud model page for qwen3.5-flashDocumented outputs
GPT-5.4 nano
Qwen3.5 Flash
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogQwen3.5 Flash
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
Qwen3.5 Flash
Reasoning
Weight access
GPT-5.4 nano
Proprietary
Qwen3.5 Flash
Proprietary
License
GPT-5.4 nano
Proprietary
Qwen3.5 Flash
Proprietary
Release date
GPT-5.4 nano
2026-03-17
Qwen3.5 Flash
2026-03-04
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
- GPT-5.4 nano has the higher public point estimate, 46.51 versus 42.31. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.01375 vs $0.0062. Cache-heavy agent loop: $0.0205 vs $0.026.
- Context tradeoff
- Qwen3.5 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.4 nano or Qwen3.5 Flash?
GPT-5.4 nano has the higher public point estimate, 46.51 versus 42.31. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.4 nano or Qwen3.5 Flash?
GPT-5.4 nano scores higher for coding on the public lane, 30.2 to 28.4. Qwen3.5 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.4 nano or Qwen3.5 Flash?
Qwen3.5 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.4 nano or Qwen3.5 Flash?
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0003 on Qwen3.5 Flash; repository review costs $0.01375 and $0.0062; the cache-heavy agent loop costs $0.0205 and $0.026. Qwen3.5 Flash 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 Qwen3.5 Flash?
Qwen3.5 Flash has the larger documented context window: 1M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence23 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceQwen3.5 Flash—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%SourceQwen3.5 Flash—Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%SourceQwen3.5 Flash—Not directly comparable
Toolathlon
GPT-5.4 nano35.5%SourceQwen3.5 Flash—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%SourceQwen3.5 Flash—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 nano41.6%SourceQwen3.5 Flash—Not directly comparable
Coding
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceQwen3.5 Flash—Not directly comparable
Vibe Code Bench
GPT-5.4 nano26.10%SourceQwen3.5 Flash—Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 nano84.0%SourceQwen3.5 Flash83.3%SourceGPT-5.4 nano leads this result
SWE-bench (Vals)
GPT-5.4 nano69.8%SourceQwen3.5 Flash64.4%SourceGPT-5.4 nano leads this result
Reasoning
Multimodal
ExtractBench (LlamaIndex)
GPT-5.4 nano74.90%SourceQwen3.5 Flash—Not directly comparable
ParseBench
GPT-5.4 nano42.24%SourceQwen3.5 Flash—Not directly comparable
MMMU-Pro
GPT-5.4 nano66.1%SourceQwen3.5 Flash—Not directly comparable
MMMU-Pro w/ Python
GPT-5.4 nano69.5%SourceQwen3.5 Flash—Not directly comparable
Knowledge
GPQA
GPT-5.4 nano82.8%SourceQwen3.5 Flash—Not directly comparable
HLE
GPT-5.4 nano37.7%SourceQwen3.5 Flash—Not directly comparable
HLE w/o tools
GPT-5.4 nano24.3%SourceQwen3.5 Flash—Not directly comparable
GPQA Diamond (Vals)
GPT-5.4 nano77.5%SourceQwen3.5 Flash82.8%SourceQwen3.5 Flash leads this result
MMLU-Pro (Vals)
GPT-5.4 nano77.2%SourceQwen3.5 Flash84.1%SourceQwen3.5 Flash leads this result
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.4 nano44.912%Qwen3.5 Flash18.246%GPT-5.4 nano leads this result
FrontierMath v2 (Tier 4)
GPT-5.4 nano12.195%SourceQwen3.5 Flash—Not directly comparable
23 public results · 5 shared
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Last updated November 11, 2026