o3-mini vs Qwen3.5 397B

Updated October 25, 2026. Rank says Qwen3.5 397B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.5 397B has the higher public point estimate, 55.42 versus 39.55. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

o3-mini

OpenAI

39.55/100

Supported · Public rank #142

90% interval 27.6–51.5

Model B

Qwen3.5 397B

Alibaba

55.42/100

Estimated · Public rank #68

Conditional range 41.1–69.8

Shared results: 3
o3-mini only: 2
Qwen3.5 397B only: 59
Like-for-like categories: 0 / 8

Supported: o3-mini · Estimated: Qwen3.5 397B. Conditional ranges do not establish rank confidence.How 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
o3-mini
o3-mini has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Qwen3.5 397B
Qwen3.5 397B 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 397B
Qwen3.5 397B 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-mini and Qwen3.5 397B 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-mini is 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-mini does not fit this workload in one request. Qwen3.5 397B does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 397B 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.

Pick the job you would use them for

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

—o3-mini—Qwen3.5 397B

Not comparable · BenchAlign v5.8

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.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

SWE-bench VerifiedCoding
Normalized gap 26.9
o3-mini:49.3%
Qwen3.5 397B:76.2%
o3-mini source Qwen3.5 397B source
GPQAKnowledge
Normalized gap 11.2
o3-mini:77.2%
Qwen3.5 397B:88.4%
o3-mini source Qwen3.5 397B source

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 v5.8 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.

Knowledge

Directional only

o3-mini: 37.0; Supported · #129/187
Qwen3.5 397B: 52.7; Estimated · #70/187
Basis: BenchAlign v5.8 lane · 2 vs 6 public rows
Reading: Directional only

Agentic

Not comparable

o3-mini: Not ranked
Qwen3.5 397B: 44.3; Estimated · #62/132
Basis: BenchAlign v5.8 lane · 0 vs 14 public rows
Reading: Not comparable

Coding

Not comparable

o3-mini: Not ranked
Qwen3.5 397B: Not ranked
Basis: BenchAlign v5.8 lane · 1 vs 6 public rows
Reading: Not comparable

Reasoning

Not comparable

o3-mini: Not ranked
Qwen3.5 397B: 74.1; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

o3-mini: Not ranked
Qwen3.5 397B: 73.4; #29/56
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

o3-mini: Not ranked
Qwen3.5 397B: 93.8; #5/17
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

o3-mini: Not ranked
Qwen3.5 397B: 79.9; #61/153
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

o3-mini: Not ranked
Qwen3.5 397B: 81.7; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for o3-mini and Qwen3.5 397B
Category	o3-mini	Qwen3.5 397B	Basis	Reading
Knowledge	37.0Supported · #129/187	52.7Estimated · #70/187	Directional onlyBenchAlign v5.8 lane · 2 vs 6 public rows	Directional only
Agentic	Not ranked	44.3Estimated · #62/132	Not comparableBenchAlign v5.8 lane · 0 vs 14 public rows	Not comparable
Coding	Not ranked	Not ranked	Not comparableBenchAlign v5.8 lane · 1 vs 6 public rows	Not comparable
Reasoning	Not ranked	74.1Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	73.4#29/56	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	93.8#5/17	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	79.9#61/153	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	81.7Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.

Supported evidence per lane · 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-mini: $0.0033; Fits in one request
Qwen3.5 397B: $0.0024; Fits in one request

Qwen3.5 397B has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

o3-mini: $0.0682; Fits in one request
Qwen3.5 397B: $0.0408; Fits in one request

Qwen3.5 397B 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-mini: $0.286; Does not fit in one request; Cached input priced at the published list-input rate
Qwen3.5 397B: $0.168; Does not fit in one request; Cached input priced at the published list-input rate

o3-mini does not fit this workload in one request. Qwen3.5 397B does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 397B 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.

Specificationo3-miniQwen3.5 397B

Context window

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

o3-mini

200K

Qwen3.5 397B

128K

API model ID

o3-mini

Not sourced

Qwen3.5 397B

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

o3-mini

Not published

Qwen3.5 397B

Not published

Documented inputs

o3-mini

Not sourced

Qwen3.5 397B

Not sourced

Documented outputs

o3-mini

Not sourced

Qwen3.5 397B

Not sourced

Provider availability

o3-mini

Not sourced

Qwen3.5 397B

Not sourced

Reasoning profile

o3-mini

Reasoning

Qwen3.5 397B

Non-Reasoning

Weight access

o3-mini

Proprietary

Qwen3.5 397B

Open Weight

License

o3-mini

Proprietary

Qwen3.5 397B

Open Weight

Release date

o3-mini

2025-01-31

Qwen3.5 397B

2026-02-16

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 397B has the higher public point estimate, 55.42 versus 39.55. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: Repository review: $0.0682 vs $0.0408. Cache-heavy agent loop: $0.286 vs $0.168.
Context tradeoff: o3-mini has the larger documented window (200K).

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

Questions

Which is better, o3-mini or Qwen3.5 397B?

Qwen3.5 397B has the higher public point estimate, 55.42 versus 39.55. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, o3-mini or Qwen3.5 397B?

O3-mini and Qwen3.5 397B are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, o3-mini or Qwen3.5 397B?

O3-mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, o3-mini or Qwen3.5 397B?

For the stated presets, chat costs $0.0033 on o3-mini and $0.0024 on Qwen3.5 397B; repository review costs $0.0682 and $0.0408; the cache-heavy agent loop costs $0.286 and $0.168. o3-mini does not fit this workload in one request. Qwen3.5 397B does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, o3-mini or Qwen3.5 397B?

o3-mini has the larger documented context window: 200K, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence64 rows

Agentic

Terminal-Bench 2.0
o3-mini—
Qwen3.5 397B52.5%
Source
Not directly comparable
BrowseComp
o3-mini—
Qwen3.5 397B69%
Source
Not directly comparable
OSWorld-Verified
o3-mini—
Qwen3.5 397B62.2%
Source
Not directly comparable
Claw-Eval
o3-mini—
Qwen3.5 397B56.8%
Source
Not directly comparable
QwenClawBench
o3-mini—
Qwen3.5 397B51.8%
Source
Not directly comparable
τ³-bench results
o3-mini—
Qwen3.5 397B68.4%
Source
Not directly comparable
VITA-Bench
o3-mini—
Qwen3.5 397B43.7%
Source
Not directly comparable
DeepPlanning
o3-mini—
Qwen3.5 397B37.6%
Source
Not directly comparable
Toolathlon
o3-mini—
Qwen3.5 397B36.3%
Source
Not directly comparable
MCP Atlas
o3-mini—
Qwen3.5 397B46.1%
Source
Not directly comparable
MCP-Tasks
o3-mini—
Qwen3.5 397B74.2%
Source
Not directly comparable
WideResearch
o3-mini—
Qwen3.5 397B74.0%
Source
Not directly comparable
Gert Labs
o3-mini—
Qwen3.5 397B46.76%
Source
Not directly comparable
ResearchClawBench
o3-mini—
Qwen3.5 397B14.2%
Source
Not directly comparable

Coding

SWE-bench Verified
o3-mini49.3%
Source
Qwen3.5 397B76.2%
Source
Qwen3.5 397B leads this result
LiveCodeBench v6
o3-mini—
Qwen3.5 397B83.6%
Source
Not directly comparable
SWE-bench Pro
o3-mini—
Qwen3.5 397B50.9%
Source
Not directly comparable
SWE Multilingual
o3-mini—
Qwen3.5 397B69.3%
Source
Not directly comparable
NL2Repo
o3-mini—
Qwen3.5 397B32.2%
Source
Not directly comparable
Terminal-Bench 2.0
o3-mini—
Qwen3.5 397B52.5%
Source
Not directly comparable

Reasoning

LongBench v2
o3-mini—
Qwen3.5 397B63.2%
Source
Not directly comparable
AI-Needle
o3-mini—
Qwen3.5 397B68.7%
Source
Not directly comparable

Multimodal

MMMU
o3-mini—
Qwen3.5 397B85.0%
Source
Not directly comparable
MMMU-Pro
o3-mini—
Qwen3.5 397B79%
Source
Not directly comparable
RealWorldQA
o3-mini—
Qwen3.5 397B83.9%
Source
Not directly comparable
OmniDocBench 1.5
o3-mini—
Qwen3.5 397B90.8%
Source
Not directly comparable
Video-MME (with subtitle)
o3-mini—
Qwen3.5 397B87.5%
Source
Not directly comparable
Video-MME (w/o subtitle)
o3-mini—
Qwen3.5 397B83.7%
Source
Not directly comparable
MathVision
o3-mini—
Qwen3.5 397B88.6%
Source
Not directly comparable
We-Math
o3-mini—
Qwen3.5 397B87.9%
Source
Not directly comparable
DynaMath
o3-mini—
Qwen3.5 397B86.3%
Source
Not directly comparable
SimpleVQA
o3-mini—
Qwen3.5 397B67.1%
Source
Not directly comparable
CharXiv
o3-mini—
Qwen3.5 397B80.8%
Source
Not directly comparable
MMLongBench-Doc
o3-mini—
Qwen3.5 397B61.5%
Source
Not directly comparable
CC-OCR
o3-mini—
Qwen3.5 397B82.0%
Source
Not directly comparable
AI2D_TEST
o3-mini—
Qwen3.5 397B93.9%
Source
Not directly comparable
CountBench
o3-mini—
Qwen3.5 397B97.2%
Source
Not directly comparable
RefCOCO (avg)
o3-mini—
Qwen3.5 397B92.3%
Source
Not directly comparable
ODINW13
o3-mini—
Qwen3.5 397B47.0%
Source
Not directly comparable
ERQA
o3-mini—
Qwen3.5 397B67.5%
Source
Not directly comparable
VideoMMMU
o3-mini—
Qwen3.5 397B84.7%
Source
Not directly comparable
MLVU (M-Avg)
o3-mini—
Qwen3.5 397B86.7%
Source
Not directly comparable
ScreenSpot Pro
o3-mini—
Qwen3.5 397B65.6%
Source
Not directly comparable
V*
o3-mini—
Qwen3.5 397B95.8%
Source
Not directly comparable

Knowledge

MMLU
o3-mini86.9%
Source
Qwen3.5 397B—
Not directly comparable
GPQA
o3-mini77.2%
Source
Qwen3.5 397B88.4%
Source
Qwen3.5 397B leads this result
SuperGPQA
o3-mini—
Qwen3.5 397B70.4%
Source
Not directly comparable
MMLU-Pro
o3-mini—
Qwen3.5 397B87.8%
Source
Not directly comparable
MMLU-Redux
o3-mini—
Qwen3.5 397B94.9%
Source
Not directly comparable
C-Eval
o3-mini—
Qwen3.5 397B93%
Source
Not directly comparable
HLE
o3-mini—
Qwen3.5 397B28.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
o3-mini—
Qwen3.5 397B84.7%
Source
Not directly comparable
NOVA-63
o3-mini—
Qwen3.5 397B59.1%
Source
Not directly comparable
INCLUDE
o3-mini—
Qwen3.5 397B85.6%
Source
Not directly comparable
PolyMath
o3-mini—
Qwen3.5 397B73.3%
Source
Not directly comparable
MAXIFE
o3-mini—
Qwen3.5 397B88.2%
Source
Not directly comparable

Instruction following

IFEval
o3-mini93.9%
Source
Qwen3.5 397B92.6%
Source
o3-mini leads this result
IFBench
o3-mini—
Qwen3.5 397B76.5%
Source
Not directly comparable

Math

AIME 2024
o3-mini87.3%
Source
Qwen3.5 397B—
Not directly comparable
AIME26
o3-mini—
Qwen3.5 397B93.3%
Source
Not directly comparable
HMMT Feb 2025
o3-mini—
Qwen3.5 397B94.8%
Source
Not directly comparable
HMMT Nov 2025
o3-mini—
Qwen3.5 397B92.7%
Source
Not directly comparable
HMMT Feb 2026
o3-mini—
Qwen3.5 397B87.9%
Source
Not directly comparable
MMAnswerBench
o3-mini—
Qwen3.5 397B80.9%
Source
Not directly comparable

64 public results · 3 shared

Related comparisons

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Last updated October 25, 2026

o3-mini vs Qwen3.5 397B

Decision reading

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Agentic

Coding

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, o3-mini or Qwen3.5 397B?

Which is better for coding, o3-mini or Qwen3.5 397B?

Which is better for agentic tasks, o3-mini or Qwen3.5 397B?

Which costs less, o3-mini or Qwen3.5 397B?

Which has the larger context window, o3-mini or Qwen3.5 397B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch o3-mini vs Qwen3.5 397B