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BenchLM

o3 vs Qwen3.5 Plus

Updated September 28, 2026. Rank says Qwen3.5 Plus is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Qwen3.5 Plus has the higher public score estimate, 48.77 versus 47.03, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo

OpenAI

47.03/100

Supported · Public rank #88

90% interval 39.9–54.2

Model B
Alibaba logo

Alibaba

48.77/100

Estimated · Public rank #83

90% interval 37.3–60.3

Shared results
2
o3 only
0
Qwen3.5 Plus only
2
Like-for-like categories
0 / 8
Supported: o3 · Estimated: Qwen3.5 PlusHow the comparison works

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

    Qwen3.5 Plus

    Qwen3.5 Plus has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Qwen3.5 Plus

    Qwen3.5 Plus has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Qwen3.5 Plus

    Qwen3.5 Plus 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

    O3 and Qwen3.5 Plus are not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    O3 and Qwen3.5 Plus are not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o3 does not fit this workload in one request. o3 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—o3—Qwen3.5 Plus

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

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

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

    Normalized gap 2.3
    o3:18.685%
    Qwen3.5 Plus:21.034%
Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.

Agentic

Not comparable
o3
Not ranked
Qwen3.5 Plus
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
o3
Not ranked
Qwen3.5 Plus
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
o3
73.7
Unranked · 2 rankable rows
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
o3
64.4
Unranked · 1 rankable row
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
o3
47.3
Estimated · #68/160
Qwen3.5 Plus
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
o3
Not ranked
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
o3
86.0
#34/124
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
o3
37.7
Unranked · 2 rankable rows
Qwen3.5 Plus
39.3
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Bars run 0–100Methodology

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

o3
$0.006
Fits in one request
Qwen3.5 Plus
$0.0016
Fits in one request

Qwen3.5 Plus has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

o3
$0.124
Fits in one request
Qwen3.5 Plus
$0.0272
Fits in one request

Qwen3.5 Plus has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

o3
$0.52
Does not fit 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

o3 does not fit this workload in one request. o3 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.

Cached input falls back to the list input rate only where a cached rate is unpublished

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.

o3

200K

Qwen3.5 Plus

1M

API model ID

o3

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.

o3

Not published

Qwen3.5 Plus

Not published

Documented inputs

o3

Not sourced

Qwen3.5 Plus

Not sourced

Documented outputs

o3

Not sourced

Qwen3.5 Plus

Not sourced

Provider availability

o3

Not sourced

Qwen3.5 Plus

Not sourced

Reasoning profile

o3

Reasoning

Qwen3.5 Plus

Reasoning

Weight access

o3

Proprietary

Qwen3.5 Plus

Proprietary

License

o3

Proprietary

Qwen3.5 Plus

Proprietary

Release date

o3

2025-04-16

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, 48.77 versus 47.03, but the 90% score intervals overlap.
Workload cost
Repository review: $0.124 vs $0.0272. Cache-heavy agent loop: $0.52 vs $0.112.
Context tradeoff
Qwen3.5 Plus has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, o3 or Qwen3.5 Plus?

Qwen3.5 Plus has the higher public score estimate, 48.77 versus 47.03, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, o3 or Qwen3.5 Plus?

O3 and Qwen3.5 Plus are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, o3 or Qwen3.5 Plus?

O3 and Qwen3.5 Plus are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, o3 or Qwen3.5 Plus?

For the stated presets, chat costs $0.006 on o3 and $0.0016 on Qwen3.5 Plus; repository review costs $0.124 and $0.0272; the cache-heavy agent loop costs $0.52 and $0.112. o3 does not fit this workload in one request. o3 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, o3 or Qwen3.5 Plus?

Qwen3.5 Plus has the larger documented context window: 1M, compared with 200K.

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

    o3—
    Qwen3.5 Plus18.5%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    o3—
    Qwen3.5 Plus15.74%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    o318.685%
    Qwen3.5 Plus21.034%

    Qwen3.5 Plus leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    o32.083%
    Qwen3.5 Plus2.083%

    Tie

4 public results · 2 shared

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Last updated September 28, 2026