Model A

OpenAI

73.27/100

Supported · Public rank #9

90% interval 71.0–75.6

GPT-5.5 vs Qwen3.5-122B-A10B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.5-122B-A10B

Alibaba

59.01/100

Supported · Public rank #78

90% interval 50.1–67.9

Decision reading

GPT-5.5 has the higher public score, 73.27 versus 59.01, and the 90% score intervals do not overlap.

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

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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.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.7 to 44.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.5-122B-A10B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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: listed-rates
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.

34 GPT-5.5 only

4 Shared

11 Qwen3.5-122B-A10B only

Shared results: 4
GPT-5.5 only: 34
Qwen3.5-122B-A10B only: 11
Like-for-like categories: 2 / 8

3 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

Like-for-like

GPT-5.5: 67.7; Supported · #8/183
Qwen3.5-122B-A10B: 44.2; Supported · #114/183
Basis: BenchAlign lane · 9 vs 1 public rows
Reading: GPT-5.5 leads

Knowledge

Like-for-like

GPT-5.5: 73.3; Supported · #7/181
Qwen3.5-122B-A10B: 50.2; Supported · #89/181
Basis: BenchAlign lane · 6 vs 3 public rows
Reading: GPT-5.5 leads

Agentic

Directional only

GPT-5.5: 63.9; Supported · #15/151
Qwen3.5-122B-A10B: 49.3; Estimated · #69/151
Basis: BenchAlign lane · 13 vs 3 public rows
Reading: Directional only

Multimodal

Directional only

GPT-5.5: 71.3; #19/48
Qwen3.5-122B-A10B: 57.0; #32/48
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Directional only

Instruction following

Directional only

GPT-5.5: 92.9; #7/120
Qwen3.5-122B-A10B: 92.7; #11/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.5: 63.5; #15/22
Qwen3.5-122B-A10B: 47.9; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.5: 69.6; Unranked · 3 rankable rows
Qwen3.5-122B-A10B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.5: Not ranked
Qwen3.5-122B-A10B: 36.8; #10/12
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.5 and Qwen3.5-122B-A10B
Category	GPT-5.5	Qwen3.5-122B-A10B	Basis	Reading
Coding	67.7Supported · #8/183	44.2Supported · #114/183	Like-for-likeBenchAlign lane · 9 vs 1 public rows	GPT-5.5 leads
Knowledge	73.3Supported · #7/181	50.2Supported · #89/181	Like-for-likeBenchAlign lane · 6 vs 3 public rows	GPT-5.5 leads
Agentic	63.9Supported · #15/151	49.3Estimated · #69/151	Directional onlyBenchAlign lane · 13 vs 3 public rows	Directional only
Multimodal	71.3#19/48	57.0#32/48	Directional onlyProvisional lane · 2 vs 1 weighted rows	Directional only
Instruction following	92.9#7/120	92.7#11/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	63.5#15/22	47.9Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Math	69.6Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	36.8#10/12	Not comparableProvisional lane · 0 vs 1 weighted rows	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.

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.

Terminal-Bench 2.0
Agentic
GPT-5.5: 82%Qwen3.5-122B-A10B: 49.4%Normalized gap 32.6GPT-5.5 source Qwen3.5-122B-A10B source
OSWorld-Verified
Agentic
GPT-5.5: 78.7%Qwen3.5-122B-A10B: 58%Normalized gap 20.7GPT-5.5 source Qwen3.5-122B-A10B source
BrowseComp
Agentic
GPT-5.5: 84.4%Qwen3.5-122B-A10B: 63.8%Normalized gap 20.6GPT-5.5 source Qwen3.5-122B-A10B source
GPQA
Knowledge
GPT-5.5: 93.6%Qwen3.5-122B-A10B: 86.6%Normalized gap 7.0GPT-5.5 source Qwen3.5-122B-A10B 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

GPT-5.5: $0.02; Fits in one request
Qwen3.5-122B-A10B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.5-122B-A10B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.5: $0.34; Fits in one request
Qwen3.5-122B-A10B: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.5-122B-A10B has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.5: $0.5; Fits in one request
Qwen3.5-122B-A10B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.5-122B-A10B 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.

SpecificationGPT-5.5Qwen3.5-122B-A10B

Context window

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

GPT-5.5

OpenAI pricing

Qwen3.5-122B-A10B

262K

API model ID

GPT-5.5

gpt-5.5

OpenAI pricing

Qwen3.5-122B-A10B

Not sourced

Cached-input rate

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

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

Qwen3.5-122B-A10B

No comparable hosted API rate

Documented inputs

GPT-5.5

Not sourced

Qwen3.5-122B-A10B

Not sourced

Documented outputs

GPT-5.5

Not sourced

Qwen3.5-122B-A10B

Not sourced

Provider availability

GPT-5.5

Not sourced

Qwen3.5-122B-A10B

Not sourced

Reasoning profile

GPT-5.5

Reasoning

Qwen3.5-122B-A10B

Reasoning

Weight access

GPT-5.5

Proprietary

Qwen3.5-122B-A10B

Open Weight

License

GPT-5.5

Proprietary

Qwen3.5-122B-A10B

Open Weight

Release date

GPT-5.5

2026-04-23

Qwen3.5-122B-A10B

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: GPT-5.5 has the higher public score, 73.27 versus 59.01, and the 90% score intervals do not overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.5 has the larger documented window (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 evidence49 rows

Agentic

Terminal-Bench 2.0
GPT-5.582%
Source
Qwen3.5-122B-A10B49.4%
Source
GPT-5.5 leads this result
CyberGym
GPT-5.581.8%
Source
Qwen3.5-122B-A10B—
Not directly comparable
BrowseComp
GPT-5.584.4%
Source
Qwen3.5-122B-A10B63.8%
Source
GPT-5.5 leads this result
OSWorld-Verified
GPT-5.578.7%
Source
Qwen3.5-122B-A10B58%
Source
GPT-5.5 leads this result
MCP Atlas
GPT-5.575.3%
Source
Qwen3.5-122B-A10B—
Not directly comparable
Toolathlon
GPT-5.555.6%
Source
Qwen3.5-122B-A10B—
Not directly comparable
τ²-bench results
GPT-5.598%
Source
Qwen3.5-122B-A10B—
Not directly comparable
Gert Labs
GPT-5.572.93%
Source
Qwen3.5-122B-A10B—
Not directly comparable
ResearchClawBench
GPT-5.517.0%
Source
Qwen3.5-122B-A10B—
Not directly comparable
OSWorld 2.0
GPT-5.513.0%
Source
Qwen3.5-122B-A10B—
Not directly comparable
JobBench
GPT-5.542.7%
Source
Qwen3.5-122B-A10B—
Not directly comparable
ExploitGym
GPT-5.513.4%
Source
Qwen3.5-122B-A10B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%
Source
Qwen3.5-122B-A10B—
Not directly comparable

Coding

SWE-bench Pro
GPT-5.558.6%
Source
Qwen3.5-122B-A10B—
Not directly comparable
Terminal-Bench 2.0
GPT-5.582.0%
Source
Qwen3.5-122B-A10B—
Not directly comparable
Vibe Code Bench
GPT-5.569.85%
Source
Qwen3.5-122B-A10B—
Not directly comparable
React Native Evals
GPT-5.584.7%
Source
Qwen3.5-122B-A10B—
Not directly comparable
cursorBench31
GPT-5.559.2%
Source
Qwen3.5-122B-A10B—
Not directly comparable
cursorBench32
GPT-5.558.4%
Source
Qwen3.5-122B-A10B—
Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%
Source
Qwen3.5-122B-A10B—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%
Source
Qwen3.5-122B-A10B—
Not directly comparable
SWE-bench (Vals)
GPT-5.582.6%
Source
Qwen3.5-122B-A10B—
Not directly comparable
SWE-bench Verified
GPT-5.5—
Qwen3.5-122B-A10B72%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
GPT-5.583.1%
Source
Qwen3.5-122B-A10B—
Not directly comparable
MRCR v2 128K-256K
GPT-5.587.5%
Source
Qwen3.5-122B-A10B—
Not directly comparable
ARC-AGI-2
GPT-5.585%
Source
Qwen3.5-122B-A10B—
Not directly comparable
ARC-AGI-3
GPT-5.50.4%
Source
Qwen3.5-122B-A10B—
Not directly comparable
LongBench v2
GPT-5.5—
Qwen3.5-122B-A10B60.2%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.593.6%
Source
Qwen3.5-122B-A10B86.6%
Source
GPT-5.5 leads this result
GPQA-D
GPT-5.593.6%
Source
Qwen3.5-122B-A10B—
Not directly comparable
HLE
GPT-5.552.2%
Source
Qwen3.5-122B-A10B—
Not directly comparable
HLE w/o tools
GPT-5.541.4%
Source
Qwen3.5-122B-A10B—
Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%
Source
Qwen3.5-122B-A10B—
Not directly comparable
MMLU-Pro (Vals)
GPT-5.588.1%
Source
Qwen3.5-122B-A10B—
Not directly comparable
MMLU-Pro
GPT-5.5—
Qwen3.5-122B-A10B86.7%
Source
Not directly comparable
SuperGPQA
GPT-5.5—
Qwen3.5-122B-A10B67.1%
Source
Not directly comparable

Math

FrontierMath (legacy)
GPT-5.551.7%
Source
Qwen3.5-122B-A10B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.551.700%
Source
Qwen3.5-122B-A10B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.535.400%
Source
Qwen3.5-122B-A10B—
Not directly comparable

Multilingual

MMLU-ProX
GPT-5.5—
Qwen3.5-122B-A10B82.2%
Source
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.581.2%
Source
Qwen3.5-122B-A10B—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.583.2%
Source
Qwen3.5-122B-A10B—
Not directly comparable
OfficeQA Pro
GPT-5.554.1%
Source
Qwen3.5-122B-A10B—
Not directly comparable
MMMU
GPT-5.5—
Qwen3.5-122B-A10B83.9%
Source
Not directly comparable
MMVU
GPT-5.5—
Qwen3.5-122B-A10B74.7%
Source
Not directly comparable
MathVision
GPT-5.5—
Qwen3.5-122B-A10B86.2%
Source
Not directly comparable
CharXiv
GPT-5.5—
Qwen3.5-122B-A10B77.2%
Source
Not directly comparable
V*
GPT-5.5—
Qwen3.5-122B-A10B93.2%
Source
Not directly comparable

Instruction following

IFEval
GPT-5.5—
Qwen3.5-122B-A10B93.4%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.5 or Qwen3.5-122B-A10B?

GPT-5.5 has the higher public score, 73.27 versus 59.01, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.5 or Qwen3.5-122B-A10B?

GPT-5.5 leads the public coding lane, 67.7 to 44.2, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, GPT-5.5 or Qwen3.5-122B-A10B?

GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 49.3. Qwen3.5-122B-A10B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.5 or Qwen3.5-122B-A10B?

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, GPT-5.5 or Qwen3.5-122B-A10B?

GPT-5.5 has the larger documented context window: 1M, compared with 262K.

Related comparisons

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Last updated September 4, 2026

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GPT-5.5 vs Qwen3.5-122B-A10B

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Agentic

Multimodal

Instruction following

Reasoning

Math

Multilingual

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, GPT-5.5 or Qwen3.5-122B-A10B?

Which is better for coding, GPT-5.5 or Qwen3.5-122B-A10B?

Which is better for agentic tasks, GPT-5.5 or Qwen3.5-122B-A10B?

Which costs less, GPT-5.5 or Qwen3.5-122B-A10B?

Which has the larger context window, GPT-5.5 or Qwen3.5-122B-A10B?

Related comparisons

Watch GPT-5.5 vs Qwen3.5-122B-A10B