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BenchLM
Model A
GPT-4.1 nano

OpenAI

41.4/100

Estimated · Public rank #177

90% interval 29.9–53.0

GPT-4.1 nano vs Qwen3.5 Plus

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.5 Plus

Alibaba

46.7/100

Estimated · Public rank #147

90% interval 32.8–60.7

Decision reading

Qwen3.5 Plus has the higher public score estimate, 46.73 versus 41.44, but the 90% score intervals overlap. Treat that as a lead, not a settled 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-4.1 nano

    GPT-4.1 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    GPT-4.1 nano

    GPT-4.1 nano has the lower estimated token cost for this stated workload. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Plus 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

    GPT-4.1 nano

    GPT-4.1 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
1
GPT-4.1 nano only
3
Qwen3.5 Plus 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.

Math

Directional only
GPT-4.1 nano
1.0
Qwen3.5 Plus
16.3
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
GPT-4.1 nano
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-4.1 nano
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-4.1 nano
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-4.1 nano
50.3
Qwen3.5 Plus
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-4.1 nano
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-4.1 nano
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-4.1 nano
83.2
Qwen3.5 Plus
Not measured
Weighted basis
1 vs 0 rows
Reading
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.

  • FrontierMath v2 (Tiers 1-3)

    Math

    GPT-4.1 nano: 1.034%Qwen3.5 Plus: 21.034%Normalized gap 20.0Shared 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-4.1 nano
$0.0003
Fits in one request
Qwen3.5 Plus
$0.0016
Fits in one request

GPT-4.1 nano has the lower modeled cost

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 Plus
$0.0272
Fits in one request

GPT-4.1 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-4.1 nano
$0.026
Fits in one request
Cached input priced at the published list-input rate
Qwen3.5 Plus
$0.112
Fits in one request
Cached input priced at the published list-input rate

GPT-4.1 nano has the lower modeled cost

GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Plus 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.

Context window

Maximum documented context; output-token limits may be lower.

GPT-4.1 nano

1M

Qwen3.5 Plus

1M

API model ID

GPT-4.1 nano

Not sourced

Qwen3.5 Plus

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 Plus

Not published

Documented inputs

GPT-4.1 nano

Not sourced

Qwen3.5 Plus

Not sourced

Documented outputs

GPT-4.1 nano

Not sourced

Qwen3.5 Plus

Not sourced

Provider availability

GPT-4.1 nano

Not sourced

Qwen3.5 Plus

Not sourced

Reasoning profile

GPT-4.1 nano

Non-Reasoning

Qwen3.5 Plus

Reasoning

Weight access

GPT-4.1 nano

Proprietary

Qwen3.5 Plus

Proprietary

License

GPT-4.1 nano

Proprietary

Qwen3.5 Plus

Proprietary

Release date

GPT-4.1 nano

2025-04-14

Qwen3.5 Plus

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 Plus has the higher public score estimate, 46.73 versus 41.44, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0062 vs $0.0272. Cache-heavy agent loop: $0.026 vs $0.112.
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 evidence7 rows

Agentic

  • JobBench

    GPT-4.1 nano—
    Qwen3.5 Plus18.5%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    GPT-4.1 nano—
    Qwen3.5 Plus15.74%
    Source

    Not directly comparable

Knowledge

  • MMLU

    GPT-4.1 nano80.1%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • GPQA

    GPT-4.1 nano50.3%
    Source
    Qwen3.5 Plus—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GPT-4.1 nano1.034%
    Qwen3.5 Plus21.034%

    Qwen3.5 Plus leads this result

  • FrontierMath v2 (Tier 4)

    GPT-4.1 nano—
    Qwen3.5 Plus2.083%
    Source

    Not directly comparable

Instruction following

  • IFEval

    GPT-4.1 nano83.2%
    Source
    Qwen3.5 Plus—

    Not directly comparable

Frequently asked questions

Which is better, GPT-4.1 nano or Qwen3.5 Plus?

Qwen3.5 Plus has the higher public score estimate, 46.73 versus 41.44, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-4.1 nano or Qwen3.5 Plus?

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-4.1 nano or Qwen3.5 Plus?

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-4.1 nano or Qwen3.5 Plus?

For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.0016 on Qwen3.5 Plus; repository review costs $0.0062 and $0.0272; the cache-heavy agent loop costs $0.026 and $0.112. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Plus 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 Plus?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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