Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-4.1 nano and Qwen3.5 Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
GPT-4.1 nano vs Qwen3.5 Flash
Updated September 29, 2026. Rank says Qwen3.5 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Qwen3.5 Flash has the higher public score, 45.47 versus 24.89, and the 90% score intervals do not overlap. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- GPT-4.1 nano only
- 3
- Qwen3.5 Flash only
- 5
- Like-for-like categories
- 0 / 8
Estimated: GPT-4.1 nano and Qwen3.5 FlashHow 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 nano and Qwen3.5 Flash are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallbackRepository review cost
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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.
14.8GPT-4.1 nano26.5Qwen3.5 Flash
Directional only · BenchAlign v5.7
Qwen3.5 Flash scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 GPT-4.1 nano only
1 Shared
5 Qwen3.5 Flash only
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 5.2GPT-4.1 nano:1.034%Qwen3.5 Flash:6.207%
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 v5.7 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
Directional only- GPT-4.1 nano
- 14.8
- Estimated · #137/143
- Qwen3.5 Flash
- 26.5
- Estimated · #103/143
- Basis
- BenchAlign v5.7 lane · 0 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-4.1 nano
- 25.2
- Supported · #162/169
- Qwen3.5 Flash
- 43.1
- Estimated · #85/169
- Basis
- BenchAlign v5.7 lane · 2 vs 2 public rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4.1 nano
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1 nano
- 36.1
- Unranked · 2 rankable rows
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 nano
- 26.4
- Unranked · 1 rankable row
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 nano
- Not ranked
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4.1 nano
- 34.6
- #109/124
- Qwen3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1 nano
- 25.4
- Unranked · 1 rankable row
- Qwen3.5 Flash
- 28.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 nano | Qwen3.5 Flash | Basis | Reading |
|---|---|---|---|---|
| Coding | 14.8Estimated · #137/143 | 26.5Estimated · #103/143 | Directional onlyBenchAlign v5.7 lane · 0 vs 2 public rows | Directional only |
| Knowledge | 25.2Supported · #162/169 | 43.1Estimated · #85/169 | Directional onlyBenchAlign v5.7 lane · 2 vs 2 public rows | Directional only |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 36.1Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 26.4Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 34.6#109/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 25.4Unranked · 1 rankable row | 28.4Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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-4.1 nano
- $0.0003
- Fits in one request
- Qwen3.5 Flash
- $0.0003
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1 nano
- $0.0062
- Fits in one request
- Qwen3.5 Flash
- $0.0062
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4.1 nano
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
- Qwen3.5 Flash
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
Modeled costs are equal
GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
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-4.1 nanoQwen3.5 Flash
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 nano
1M
Qwen3.5 Flash
1M
API model ID
GPT-4.1 nano
Not sourced
Qwen3.5 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-4.1 nano
Not published
Qwen3.5 Flash
Not published
Documented inputs
GPT-4.1 nano
Not sourced
Qwen3.5 Flash
Not sourced
Documented outputs
GPT-4.1 nano
Not sourced
Qwen3.5 Flash
Not sourced
Provider availability
GPT-4.1 nano
Not sourced
Qwen3.5 Flash
Not sourced
Reasoning profile
GPT-4.1 nano
Non-Reasoning
Qwen3.5 Flash
Reasoning
Weight access
GPT-4.1 nano
Proprietary
Qwen3.5 Flash
Proprietary
License
GPT-4.1 nano
Proprietary
Qwen3.5 Flash
Proprietary
Release date
GPT-4.1 nano
2025-04-14
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
- Qwen3.5 Flash has the higher public score, 45.47 versus 24.89, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0062 vs $0.0062. Cache-heavy agent loop: $0.026 vs $0.026.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-4.1 nano or Qwen3.5 Flash?
Qwen3.5 Flash has the higher public score, 45.47 versus 24.89, 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, GPT-4.1 nano or Qwen3.5 Flash?
Qwen3.5 Flash scores higher for coding on the public lane, 26.5 to 14.8. GPT-4.1 nano and Qwen3.5 Flash are 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-4.1 nano or Qwen3.5 Flash?
GPT-4.1 nano and Qwen3.5 Flash are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4.1 nano or Qwen3.5 Flash?
For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.0003 on Qwen3.5 Flash; repository review costs $0.0062 and $0.0062; the cache-heavy agent loop costs $0.026 and $0.026. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 nano or Qwen3.5 Flash?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence9 rows
Coding
Knowledge
Instruction following
IFEval
GPT-4.1 nano83.2%SourceQwen3.5 Flash—Not directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-4.1 nano1.034%Qwen3.5 Flash6.207%Qwen3.5 Flash leads this result
FrontierMath v2 (Tier 4)
GPT-4.1 nano—Qwen3.5 Flash0.000%SourceNot directly comparable
9 public results · 1 shared
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Last updated September 29, 2026