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BenchLM

Gemini 1.5 Pro vs Qwen3.8 Max

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Google logo
Model A
Gemini 1.5 Pro

Google

33.6/100

Supported · Public rank #221

90% interval 19.1–48.1

Alibaba logo
Model B
Qwen3.8 Max

Alibaba

71.76/100

Supported · Public rank #11

90% interval 68.1–75.4

Updated September 15, 2026. Rank says Qwen3.8 Max is ahead. Price, access, and your workload can each overturn that. 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

    Gemini 1.5 Pro is 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

    Gemini 1.5 Pro 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
0
Gemini 1.5 Pro only
0
Qwen3.8 Max only
60
Like-for-like categories
0 / 8

2 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
Gemini 1.5 Pro
34.2
Estimated · #130/152
Qwen3.8 Max
60.8
Supported · #20/152
Basis
BenchAlign lane · 0 vs 12 public rows
Reading
Directional only

Knowledge

Directional only
Gemini 1.5 Pro
33.6
Estimated · #167/183
Qwen3.8 Max
68.8
Supported · #17/183
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Directional only

Agentic

Not comparable
Gemini 1.5 Pro
Not ranked
Qwen3.8 Max
67.3
Supported · #9/153
Basis
BenchAlign lane · 0 vs 15 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 1.5 Pro
Not ranked
Qwen3.8 Max
86.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 1.5 Pro
Not ranked
Qwen3.8 Max
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 1.5 Pro
Not ranked
Qwen3.8 Max
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 1.5 Pro
41.1
Unranked · 1 rankable row
Qwen3.8 Max
87.4
#5/48
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 1.5 Pro
Not ranked
Qwen3.8 Max
90.7
#18/123
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Gemini 1.5 Pro
$0.00375
Fits in one request
Qwen3.8 Max
API rate not published
Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 1.5 Pro
$0.0775
Fits in one request
Qwen3.8 Max
API rate not published
Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 1.5 Pro
$0.325
Fits in one request
Cached input priced at the published list-input rate
Qwen3.8 Max
API rate not published
Fits in one request
Cached-input rate unavailable

Gemini 1.5 Pro has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8 Max 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.

Gemini 1.5 Pro

Not published

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Gemini 1.5 Pro

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Gemini 1.5 Pro

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Gemini 1.5 Pro

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Gemini 1.5 Pro

Non-Reasoning

Qwen3.8 Max

Reasoning

Weight access

Gemini 1.5 Pro

Proprietary

Qwen3.8 Max

Open Weight

License

Gemini 1.5 Pro

Proprietary

Qwen3.8 Max

Open Weight

Release date

Gemini 1.5 Pro

2024-02-15

Qwen3.8 Max

2026-08-03

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
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 evidence60 rows

Agentic

  • Terminal-Bench 2.1

    Gemini 1.5 Pro—
    Qwen3.8 Max86.6%
    Source

    Not directly comparable

  • CoWorkBench

    Gemini 1.5 Pro—
    Qwen3.8 Max74.8%
    Source

    Not directly comparable

  • JobBench

    Gemini 1.5 Pro—
    Qwen3.8 Max53.4%
    Source

    Not directly comparable

  • skillsBench

    Gemini 1.5 Pro—
    Qwen3.8 Max70.2%
    Source

    Not directly comparable

  • Agents' Last Exam

    Gemini 1.5 Pro—
    Qwen3.8 Max52.4%
    Source

    Not directly comparable

  • AutomationBench

    Gemini 1.5 Pro—
    Qwen3.8 Max27.3%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Gemini 1.5 Pro—
    Qwen3.8 Max72.5%
    Source

    Not directly comparable

  • WideResearch

    Gemini 1.5 Pro—
    Qwen3.8 Max81.9%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemini 1.5 Pro—
    Qwen3.8 Max56.2%
    Source

    Not directly comparable

  • OSWorld-Verified

    Gemini 1.5 Pro—
    Qwen3.8 Max86.1%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemini 1.5 Pro—
    Qwen3.8 Max19.4%
    Source

    Not directly comparable

  • WebArena-Verified

    Gemini 1.5 Pro—
    Qwen3.8 Max66.8%
    Source

    Not directly comparable

  • AndroidWorld

    Gemini 1.5 Pro—
    Qwen3.8 Max85.3%
    Source

    Not directly comparable

  • MobileWorld

    Gemini 1.5 Pro—
    Qwen3.8 Max77.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 1.5 Pro—
    Qwen3.8 Max67.4%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Gemini 1.5 Pro—
    Qwen3.8 Max86.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemini 1.5 Pro—
    Qwen3.8 Max67.7%
    Source

    Not directly comparable

  • DeepSWE

    Gemini 1.5 Pro—
    Qwen3.8 Max56.6%
    Source

    Not directly comparable

  • NL2Repo

    Gemini 1.5 Pro—
    Qwen3.8 Max55.9%
    Source

    Not directly comparable

  • FrontierSWE

    Gemini 1.5 Pro—
    Qwen3.8 Max73.5%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Gemini 1.5 Pro—
    Qwen3.8 Max41.0%
    Source

    Not directly comparable

  • PaperBench

    Gemini 1.5 Pro—
    Qwen3.8 Max93.0%
    Source

    Not directly comparable

  • VulcanBench v3

    Gemini 1.5 Pro—
    Qwen3.8 Max81.2%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Gemini 1.5 Pro—
    Qwen3.8 Max60.8%
    Source

    Not directly comparable

  • FrontierSWE v2

    Gemini 1.5 Pro—
    Qwen3.8 Max15.8%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 1.5 Pro—
    Qwen3.8 Max87.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 1.5 Pro—
    Qwen3.8 Max85.6%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 1.5 Pro—
    Qwen3.8 Max92.9%
    Source

    Not directly comparable

  • LongBench v2

    Gemini 1.5 Pro—
    Qwen3.8 Max66.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemini 1.5 Pro—
    Qwen3.8 Max92.6%
    Source

    Not directly comparable

  • GPQA-D

    Gemini 1.5 Pro—
    Qwen3.8 Max92.6%
    Source

    Not directly comparable

  • HLE

    Gemini 1.5 Pro—
    Qwen3.8 Max43.6%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 1.5 Pro—
    Qwen3.8 Max43.6%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 1.5 Pro—
    Qwen3.8 Max93.7%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 1.5 Pro—
    Qwen3.8 Max88.6%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 1.5 Pro—
    Qwen3.8 Max82.3%
    Source

    Not directly comparable

  • MathVision

    Gemini 1.5 Pro—
    Qwen3.8 Max95.2%
    Source

    Not directly comparable

  • MathVision w/ Python

    Gemini 1.5 Pro—
    Qwen3.8 Max97.7%
    Source

    Not directly comparable

  • BabyVision

    Gemini 1.5 Pro—
    Qwen3.8 Max82.0%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Gemini 1.5 Pro—
    Qwen3.8 Max91.3%
    Source

    Not directly comparable

  • ZeroBench

    Gemini 1.5 Pro—
    Qwen3.8 Max24.0%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Gemini 1.5 Pro—
    Qwen3.8 Max49.0%
    Source

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 1.5 Pro—
    Qwen3.8 Max80.4%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Gemini 1.5 Pro—
    Qwen3.8 Max84.5%
    Source

    Not directly comparable

  • Vision2Web

    Gemini 1.5 Pro—
    Qwen3.8 Max69.0%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Gemini 1.5 Pro—
    Qwen3.8 Max88.4%
    Source

    Not directly comparable

  • CharXiv

    Gemini 1.5 Pro—
    Qwen3.8 Max93.5%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Gemini 1.5 Pro—
    Qwen3.8 Max92.1%
    Source

    Not directly comparable

  • OCRBench V2

    Gemini 1.5 Pro—
    Qwen3.8 Max74.2%
    Source

    Not directly comparable

  • CC-OCR

    Gemini 1.5 Pro—
    Qwen3.8 Max79.6%
    Source

    Not directly comparable

  • RealWorldQA

    Gemini 1.5 Pro—
    Qwen3.8 Max88.0%
    Source

    Not directly comparable

  • ERQA

    Gemini 1.5 Pro—
    Qwen3.8 Max77.8%
    Source

    Not directly comparable

  • SimpleVQA

    Gemini 1.5 Pro—
    Qwen3.8 Max75.0%
    Source

    Not directly comparable

  • PerceptionBench

    Gemini 1.5 Pro—
    Qwen3.8 Max63.5%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Gemini 1.5 Pro—
    Qwen3.8 Max90.4%
    Source

    Not directly comparable

  • VideoMMMU

    Gemini 1.5 Pro—
    Qwen3.8 Max88.7%
    Source

    Not directly comparable

  • MMVU

    Gemini 1.5 Pro—
    Qwen3.8 Max82.4%
    Source

    Not directly comparable

  • MLVU (M-Avg)

    Gemini 1.5 Pro—
    Qwen3.8 Max90.8%
    Source

    Not directly comparable

  • LVBench

    Gemini 1.5 Pro—
    Qwen3.8 Max81.8%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Gemini 1.5 Pro—
    Qwen3.8 Max82.8%
    Source

    Not directly comparable

Questions

Which is better, Gemini 1.5 Pro or Qwen3.8 Max?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 1.5 Pro or Qwen3.8 Max?

Qwen3.8 Max scores higher for coding on the public lane, 60.8 to 34.2. Gemini 1.5 Pro 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, Gemini 1.5 Pro or Qwen3.8 Max?

Gemini 1.5 Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 1.5 Pro or Qwen3.8 Max?

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, Gemini 1.5 Pro or Qwen3.8 Max?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 15, 2026

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