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

Updated October 20, 2026. Rank says GPT-5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.5 has the higher public point estimate, 69.22 versus 41.69. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.5

OpenAI

69.22/100

Supported · Public rank #20

90% interval 63.0–75.5

Model B

Qwen3.5-122B-A10B

Alibaba

41.69/100

Estimated · Public rank #129

Conditional range 32.0–51.4

Shared results: 6
GPT-5.5 only: 38
Qwen3.5-122B-A10B only: 12
Like-for-like categories: 2 / 8

Supported: GPT-5.5 · Estimated: Qwen3.5-122B-A10B. 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.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 has the higher public coding point estimate, 62 to 32.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
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

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.

62.0GPT-5.532.6Qwen3.5-122B-A10B

Like-for-like · BenchAlign v5.8

GPT-5.5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

Terminal-Bench 2.0Agentic
Normalized gap 32.6
GPT-5.5:82%
Qwen3.5-122B-A10B:49.4%
GPT-5.5 source Qwen3.5-122B-A10B source
OSWorld-VerifiedAgentic
Normalized gap 20.7
GPT-5.5:78.7%
Qwen3.5-122B-A10B:58%
GPT-5.5 source Qwen3.5-122B-A10B source
BrowseCompAgentic
Normalized gap 20.6
GPT-5.5:84.4%
Qwen3.5-122B-A10B:63.8%
GPT-5.5 source Qwen3.5-122B-A10B source
GPQAKnowledge
Normalized gap 7.0
GPT-5.5:93.6%
Qwen3.5-122B-A10B:86.6%
GPT-5.5 source Qwen3.5-122B-A10B source
MMMU-ProMultimodal
Normalized gap 4.3
GPT-5.5:81.2%
Qwen3.5-122B-A10B:76.9%
GPT-5.5 source Qwen3.5-122B-A10B 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.

Coding

Like-for-like

GPT-5.5: 62.0; Supported · #14/152
Qwen3.5-122B-A10B: 32.6; Supported · #85/152
Basis: BenchAlign v5.8 lane · 10 vs 3 public rows
Reading: GPT-5.5 leads

Knowledge

Like-for-like

GPT-5.5: 72.1; Supported · #15/187
Qwen3.5-122B-A10B: 43.8; Supported · #105/187
Basis: BenchAlign v5.8 lane · 6 vs 3 public rows
Reading: GPT-5.5 leads

Agentic

Directional only

GPT-5.5: 62.7; Supported · #23/131
Qwen3.5-122B-A10B: 24.0; Estimated · #108/131
Basis: BenchAlign v5.8 lane · 15 vs 3 public rows
Reading: Directional only

Multimodal

Directional only

GPT-5.5: 75.5; #23/55
Qwen3.5-122B-A10B: 67.8; #36/55
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Directional only

Instruction following

Directional only

GPT-5.5: 93.3; #8/149
Qwen3.5-122B-A10B: 93.0; #12/149
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.5: 70.3; #17/27
Qwen3.5-122B-A10B: 68.8; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.5: Not ranked
Qwen3.5-122B-A10B: 87.1; #9/17
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.5: 91.2; Unranked · 3 rankable rows
Qwen3.5-122B-A10B: Not ranked
Basis: Provisional lane · 2 vs 0 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	62.0Supported · #14/152	32.6Supported · #85/152	Like-for-likeBenchAlign v5.8 lane · 10 vs 3 public rows	GPT-5.5 leads
Knowledge	72.1Supported · #15/187	43.8Supported · #105/187	Like-for-likeBenchAlign v5.8 lane · 6 vs 3 public rows	GPT-5.5 leads
Agentic	62.7Supported · #23/131	24.0Estimated · #108/131	Directional onlyBenchAlign v5.8 lane · 15 vs 3 public rows	Directional only
Multimodal	75.5#23/55	67.8#36/55	Directional onlyProvisional lane · 2 vs 2 weighted rows	Directional only
Instruction following	93.3#8/149	93.0#12/149	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	70.3#17/27	68.8Unranked · 3 rankable rows	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	87.1#9/17	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	91.2Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 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

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.

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.

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 point estimate, 69.22 versus 41.69. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
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.

Questions

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

GPT-5.5 has the higher public point estimate, 69.22 versus 41.69. Their conditional score ranges do not 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, GPT-5.5 or Qwen3.5-122B-A10B?

GPT-5.5 has the higher public coding point estimate, 62 to 32.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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, 62.7 to 24. 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.

Benchmark evidence

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

Browse raw public benchmark evidence56 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
ApprenticeBench
GPT-5.520%
Source
Qwen3.5-122B-A10B—
Not directly comparable
MazeBench
GPT-5.50%
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-A10B49.4%
Source
GPT-5.5 leads this result
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
CursorBench 3.2
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
PostTrainBench v1.1
GPT-5.527.2%
Source
Qwen3.5-122B-A10B—
Not directly comparable
SWE-bench Verified
GPT-5.5—
Qwen3.5-122B-A10B72%
Source
Not directly comparable
LiveCodeBench v6
GPT-5.5—
Qwen3.5-122B-A10B78.9%
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
ARC-AGI-1
GPT-5.595.00%
Source
Qwen3.5-122B-A10B—
Not directly comparable
LongBench v2
GPT-5.5—
Qwen3.5-122B-A10B60.2%
Source
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
GPT-5.593.57%
Source
Qwen3.5-122B-A10B—
Not directly comparable
ParseBench
GPT-5.567.76%
Source
Qwen3.5-122B-A10B—
Not directly comparable
MMMU-Pro
GPT-5.581.2%
Source
Qwen3.5-122B-A10B76.9%
Source
GPT-5.5 leads this result
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

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

Multilingual

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

Instruction following

IFEval
GPT-5.5—
Qwen3.5-122B-A10B93.4%
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-5.585.263%
Source
Qwen3.5-122B-A10B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.572.500%
Source
Qwen3.5-122B-A10B—
Not directly comparable

56 public results · 6 shared

Related comparisons

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Last updated October 20, 2026

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

Decision reading

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Multimodal

Instruction following

Reasoning

Multilingual

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, 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?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

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