Skip to main content
Radar

Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.

Start free brief
Model A
Llama 4 Scout

Meta

39.2/100

Supported · Public rank #192

90% interval 20.6–57.9

Llama 4 Scout 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 39.24, 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.

  • Long documents

    Prompts that approach the documented context limit

    Llama 4 Scout

    Llama 4 Scout has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • 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

  • 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
Llama 4 Scout only
0
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
Llama 4 Scout
Not measured
Qwen3.5 Plus
16.3
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Llama 4 Scout
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Llama 4 Scout
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Llama 4 Scout
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Llama 4 Scout
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Llama 4 Scout
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Llama 4 Scout
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Llama 4 Scout
Not measured
Qwen3.5 Plus
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.

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

    Llama 4 Scout: 0.000%Qwen3.5 Plus: 21.034%Normalized gap 21.0Shared 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

Llama 4 Scout
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.5 Plus
$0.0016
Fits in one request

Llama 4 Scout has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Llama 4 Scout
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.5 Plus
$0.0272
Fits in one request

Llama 4 Scout has no comparable published API token rate.

Cache-heavy agent loop

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

Llama 4 Scout
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Qwen3.5 Plus
$0.112
Fits in one request
Cached input priced at the published list-input rate

Qwen3.5 Plus has no published cached-input rate, so cached tokens use its listed input rate. Llama 4 Scout 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.

Context window

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

Llama 4 Scout

10M

Qwen3.5 Plus

1M

API model ID

Llama 4 Scout

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.

Llama 4 Scout

No comparable hosted API rate

Qwen3.5 Plus

Not published

Documented inputs

Llama 4 Scout

Not sourced

Qwen3.5 Plus

Not sourced

Documented outputs

Llama 4 Scout

Not sourced

Qwen3.5 Plus

Not sourced

Provider availability

Llama 4 Scout

Not sourced

Qwen3.5 Plus

Not sourced

Reasoning profile

Llama 4 Scout

Non-Reasoning

Qwen3.5 Plus

Reasoning

Weight access

Llama 4 Scout

Open Weight

Qwen3.5 Plus

Proprietary

License

Llama 4 Scout

Open Weight

Qwen3.5 Plus

Proprietary

Release date

Llama 4 Scout

2026-02-28

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 39.24, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Llama 4 Scout has the larger documented window (10M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Llama 4 Scout
API / mo$0
Self-host / mo$2,278
Break-even
Qwen3.5 Plus
API / mo$2,100
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence4 rows

Agentic

  • JobBench

    Llama 4 Scout
    Qwen3.5 Plus18.5%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Llama 4 Scout
    Qwen3.5 Plus15.74%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Llama 4 Scout0.000%
    Qwen3.5 Plus21.034%

    Qwen3.5 Plus leads this result

  • FrontierMath v2 (Tier 4)

    Llama 4 Scout
    Qwen3.5 Plus2.083%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Llama 4 Scout or Qwen3.5 Plus?

Qwen3.5 Plus has the higher public score estimate, 46.73 versus 39.24, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Llama 4 Scout 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, Llama 4 Scout 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, Llama 4 Scout or Qwen3.5 Plus?

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, Llama 4 Scout or Qwen3.5 Plus?

Llama 4 Scout has the larger documented context window: 10M, compared with 1M.

Related comparisons

Last updated August 13, 2026

Watch Llama 4 Scout vs Qwen3.5 Plus

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.