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Model A
Gemini 1.5 Pro

Google

Evidence status unavailable

90% interval unavailable

Gemini 1.5 Pro vs Qwen3.5 Flash

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

Model B
Qwen3.5 Flash

Alibaba

47.1/100

Supported · Public rank #143

90% interval 24.2–70.0

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 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

    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-rates

  • Cache-heavy agent loop cost

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

    Qwen3.5 Flash

    Qwen3.5 Flash has the lower estimated token cost for this stated workload. Gemini 1.5 Pro 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.

    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-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
0
Gemini 1.5 Pro only
0
Qwen3.5 Flash only
2
Like-for-like categories
0 / 8

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.

Agentic

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
4.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 1.5 Pro
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 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.

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
Qwen3.5 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

Gemini 1.5 Pro
$0.0775
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

Gemini 1.5 Pro
$0.325
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

Qwen3.5 Flash has the lower modeled cost

Gemini 1.5 Pro 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.

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.

Gemini 1.5 Pro

1M

Qwen3.5 Flash

1M

API model ID

Gemini 1.5 Pro

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.

Gemini 1.5 Pro

Not published

Qwen3.5 Flash

Not published

Documented inputs

Gemini 1.5 Pro

Not sourced

Qwen3.5 Flash

Not sourced

Documented outputs

Gemini 1.5 Pro

Not sourced

Qwen3.5 Flash

Not sourced

Provider availability

Gemini 1.5 Pro

Not sourced

Qwen3.5 Flash

Not sourced

Reasoning profile

Gemini 1.5 Pro

Non-Reasoning

Qwen3.5 Flash

Reasoning

Weight access

Gemini 1.5 Pro

Proprietary

Qwen3.5 Flash

Proprietary

License

Gemini 1.5 Pro

Proprietary

Qwen3.5 Flash

Proprietary

Release date

Gemini 1.5 Pro

2024-02-15

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.0775 vs $0.0062. Cache-heavy agent loop: $0.325 vs $0.026.
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 evidence2 rows

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 1.5 Pro
    Qwen3.5 Flash6.207%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 1.5 Pro
    Qwen3.5 Flash0.000%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 1.5 Pro or Qwen3.5 Flash?

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 Qwen3.5 Flash?

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, Gemini 1.5 Pro or Qwen3.5 Flash?

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, Gemini 1.5 Pro or Qwen3.5 Flash?

For the stated presets, chat costs $0.00375 on Gemini 1.5 Pro and $0.0003 on Qwen3.5 Flash; repository review costs $0.0775 and $0.0062; the cache-heavy agent loop costs $0.325 and $0.026. Gemini 1.5 Pro 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, Gemini 1.5 Pro or Qwen3.5 Flash?

Both models list the same context window, 1M.

Related comparisons

Last updated August 13, 2026

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