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BenchLM

GPT-4.1 nano vs Qwen3.8-Omni-Flash

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

OpenAI logo
Model A
GPT-4.1 nano

OpenAI

27.21/100

Estimated · Public rank #233

90% interval 21.5–33.0

Alibaba logo
Model B
Qwen3.8-Omni-Flash

Alibaba

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    GPT-4.1 nano and Qwen3.8-Omni-Flash are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Qwen3.8-Omni-Flash is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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: rate-fallback

  • 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.

Shared results
1
GPT-4.1 nano only
3
Qwen3.8-Omni-Flash only
18
Like-for-like categories
0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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
31.1
Estimated · #139/154
Qwen3.8-Omni-Flash
53.5
Estimated · #45/154
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Directional only

Knowledge

Directional only
GPT-4.1 nano
29.7
Supported · #180/184
Qwen3.8-Omni-Flash
54.9
Estimated · #54/184
Basis
BenchAlign lane · 2 vs 3 public rows
Reading
Directional only

Instruction following

Directional only
GPT-4.1 nano
34.6
#109/124
Qwen3.8-Omni-Flash
87.7
#29/124
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Agentic

Not comparable
GPT-4.1 nano
33.6
Estimated · #139/154
Qwen3.8-Omni-Flash
Not ranked
Basis
BenchAlign lane · 0 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-4.1 nano
35.0
Unranked · 2 rankable rows
Qwen3.8-Omni-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
Qwen3.8-Omni-Flash
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-4.1 nano
Not ranked
Qwen3.8-Omni-Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-4.1 nano
25.4
Unranked · 1 rankable row
Qwen3.8-Omni-Flash
85.1
Unranked · 7 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

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
Qwen3.8-Omni-Flash
API rate not published
Fits in one request

Qwen3.8-Omni-Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-4.1 nano
$0.0062
Fits in one request
Qwen3.8-Omni-Flash
API rate not published
Fits in one request

Qwen3.8-Omni-Flash has no comparable published API token rate.

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
Qwen3.8-Omni-Flash
API rate not published
Fits in one request
Cached-input rate unavailable

GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Omni-Flash 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.

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

Qwen3.8-Omni-Flash

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

GPT-4.1 nano

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Documented outputs

GPT-4.1 nano

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Provider availability

GPT-4.1 nano

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Reasoning profile

GPT-4.1 nano

Non-Reasoning

Qwen3.8-Omni-Flash

Reasoning

Weight access

GPT-4.1 nano

Proprietary

Qwen3.8-Omni-Flash

Proprietary

License

GPT-4.1 nano

Proprietary

Qwen3.8-Omni-Flash

Proprietary

Release date

GPT-4.1 nano

2025-04-14

Qwen3.8-Omni-Flash

2026-09-18

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
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 evidence22 rows

Agentic

  • CoWorkBench

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash75.3%
    Source

    Not directly comparable

  • AndroidWorld

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash87.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash63.3%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash80.5%
    Source

    Not directly comparable

  • NL2Repo

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash48.9%
    Source

    Not directly comparable

  • DeepSWE

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash57.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash92.6%
    Source

    Not directly comparable

Knowledge

  • MMLU

    GPT-4.1 nano80.1%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • GPQA

    GPT-4.1 nano50.3%
    Source
    Qwen3.8-Omni-Flash91%
    Source

    Qwen3.8-Omni-Flash leads this result

  • GPQA-D

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash91.0%
    Source

    Not directly comparable

  • HLE

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash36.5%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-4.1 nano1.034%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

Multimodal

  • Vision2Web

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash62.9%
    Source

    Not directly comparable

  • ERQA

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash71.0%
    Source

    Not directly comparable

  • LVBench

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash76.9%
    Source

    Not directly comparable

  • RealWorldQA

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash87.7%
    Source

    Not directly comparable

  • MathVision

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash91.8%
    Source

    Not directly comparable

  • MathVision w/ Python

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash96.2%
    Source

    Not directly comparable

  • CharXiv w/o tools

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash83.5%
    Source

    Not directly comparable

  • CharXiv

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash91.4%
    Source

    Not directly comparable

Instruction following

  • IFEval

    GPT-4.1 nano83.2%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • IFBench

    GPT-4.1 nano—
    Qwen3.8-Omni-Flash81.5%
    Source

    Not directly comparable

Questions

Which is better, GPT-4.1 nano or Qwen3.8-Omni-Flash?

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-4.1 nano or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash scores higher for coding on the public lane, 53.5 to 31.1. GPT-4.1 nano and Qwen3.8-Omni-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 Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash is 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 Qwen3.8-Omni-Flash?

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-4.1 nano or Qwen3.8-Omni-Flash?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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