Coding work
Code generation, repair, and software-engineering tasks
GPT-5.2-Codex
GPT-5.2-Codex leads on the public coding lane, 47.1 to 35.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GPT-5.2-Codex vs Qwen3.5-122B-A10B
Updated September 27, 2026. Rank says GPT-5.2-Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- GPT-5.2-Codex only
- 5
- Qwen3.5-122B-A10B only
- 15
- Like-for-like categories
- 1 / 8
Estimated: GPT-5.2-Codex and Qwen3.5-122B-A10BHow 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
GPT-5.2-Codex
GPT-5.2-Codex 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
GPT-5.2-Codex and Qwen3.5-122B-A10B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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-ratesCache-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-fallbackRepository 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
47.1GPT-5.2-Codex35.7Qwen3.5-122B-A10B
Like-for-like · BenchAlign v5.7
GPT-5.2-Codex leads the like-for-like coding row, although the 90% intervals overlap.
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.
5 GPT-5.2-Codex only
15 Qwen3.5-122B-A10B only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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.
Coding
Like-for-like- GPT-5.2-Codex
- 47.1
- Supported · #43/135
- Qwen3.5-122B-A10B
- 35.7
- Supported · #73/135
- Basis
- BenchAlign v5.7 lane · 3 vs 1 public rows
- Reading
- GPT-5.2-Codex leads · intervals overlap
Agentic
Directional only- GPT-5.2-Codex
- 34.2
- Estimated · #57/105
- Qwen3.5-122B-A10B
- 23.1
- Estimated · #84/105
- Basis
- BenchAlign v5.7 lane · 2 vs 3 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.2-Codex
- 52.0
- Estimated · #54/158
- Qwen3.5-122B-A10B
- 41.5
- Supported · #86/158
- Basis
- BenchAlign v5.7 lane · 0 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.2-Codex
- 92.4
- #3/124
- Qwen3.5-122B-A10B
- 91.6
- #11/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.2-Codex
- 78.6
- Unranked · 2 rankable rows
- Qwen3.5-122B-A10B
- 49.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2-Codex
- 72.5
- Unranked · 1 rankable row
- Qwen3.5-122B-A10B
- 57.0
- #34/50
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2-Codex
- Not ranked
- Qwen3.5-122B-A10B
- 36.8
- #10/12
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2-Codex
- Not ranked
- Qwen3.5-122B-A10B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2-Codex | Qwen3.5-122B-A10B | Basis | Reading |
|---|---|---|---|---|
| Coding | 47.1Supported · #43/135 | 35.7Supported · #73/135 | Like-for-likeBenchAlign v5.7 lane · 3 vs 1 public rows | GPT-5.2-Codex leads · intervals overlap |
| Agentic | 34.2Estimated · #57/105 | 23.1Estimated · #84/105 | Directional onlyBenchAlign v5.7 lane · 2 vs 3 public rows | Directional only |
| Knowledge | 52.0Estimated · #54/158 | 41.5Supported · #86/158 | Directional onlyBenchAlign v5.7 lane · 0 vs 3 public rows | Directional only |
| Instruction following | 92.4#3/124 | 91.6#11/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 78.6Unranked · 2 rankable rows | 49.8Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 72.5Unranked · 1 rankable row | 57.0#34/50 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | 36.8#10/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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.
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-5.2-Codex
- $0.00875
- 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-5.2-Codex
- $0.1295
- 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-5.2-Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Qwen3.5-122B-A10B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate. 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-5.2-CodexQwen3.5-122B-A10B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2-Codex
400K
Qwen3.5-122B-A10B
262K
API model ID
GPT-5.2-Codex
Not sourced
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-5.2-Codex
Not published
Qwen3.5-122B-A10B
No comparable hosted API rate
Documented inputs
GPT-5.2-Codex
Not sourced
Qwen3.5-122B-A10B
Not sourced
Documented outputs
GPT-5.2-Codex
Not sourced
Qwen3.5-122B-A10B
Not sourced
Provider availability
GPT-5.2-Codex
Not sourced
Qwen3.5-122B-A10B
Not sourced
Reasoning profile
GPT-5.2-Codex
Reasoning
Qwen3.5-122B-A10B
Reasoning
Weight access
GPT-5.2-Codex
Proprietary
Qwen3.5-122B-A10B
Open Weight
License
GPT-5.2-Codex
Proprietary
Qwen3.5-122B-A10B
Open Weight
Release date
GPT-5.2-Codex
2025-12-18
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.2-Codex has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.2-Codex or Qwen3.5-122B-A10B?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.2-Codex or Qwen3.5-122B-A10B?
GPT-5.2-Codex leads the public coding lane, 47.1 to 35.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.2-Codex or Qwen3.5-122B-A10B?
GPT-5.2-Codex scores higher for agentic tasks on the public lane, 34.2 to 23.1. GPT-5.2-Codex and Qwen3.5-122B-A10B are 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-5.2-Codex 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-5.2-Codex or Qwen3.5-122B-A10B?
GPT-5.2-Codex has the larger documented context window: 400K, 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 evidence20 rows
Agentic
Gert Labs
GPT-5.2-Codex51.79%SourceQwen3.5-122B-A10B—Not directly comparable
JobBench
GPT-5.2-Codex26.0%SourceQwen3.5-122B-A10B—Not directly comparable
Terminal-Bench 2.0
GPT-5.2-Codex—Qwen3.5-122B-A10B49.4%SourceNot directly comparable
BrowseComp
GPT-5.2-Codex—Qwen3.5-122B-A10B63.8%SourceNot directly comparable
OSWorld-Verified
GPT-5.2-Codex—Qwen3.5-122B-A10B58%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5.2-Codex37.91%SourceQwen3.5-122B-A10B—Not directly comparable
LiveCodeBench (Vals)
GPT-5.2-Codex88.0%SourceQwen3.5-122B-A10B—Not directly comparable
SWE-bench (Vals)
GPT-5.2-Codex72.4%SourceQwen3.5-122B-A10B—Not directly comparable
SWE-bench Verified
GPT-5.2-Codex—Qwen3.5-122B-A10B72%SourceNot directly comparable
Reasoning
LongBench v2
GPT-5.2-Codex—Qwen3.5-122B-A10B60.2%SourceNot directly comparable
Multimodal
MMMU
GPT-5.2-Codex—Qwen3.5-122B-A10B83.9%SourceNot directly comparable
MMVU
GPT-5.2-Codex—Qwen3.5-122B-A10B74.7%SourceNot directly comparable
MathVision
GPT-5.2-Codex—Qwen3.5-122B-A10B86.2%SourceNot directly comparable
CharXiv
GPT-5.2-Codex—Qwen3.5-122B-A10B77.2%SourceNot directly comparable
V*
GPT-5.2-Codex—Qwen3.5-122B-A10B93.2%SourceNot directly comparable
Knowledge
Multilingual
MMLU-ProX
GPT-5.2-Codex—Qwen3.5-122B-A10B82.2%SourceNot directly comparable
Instruction following
IFEval
GPT-5.2-Codex—Qwen3.5-122B-A10B93.4%SourceNot directly comparable
20 public results · 0 shared
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Last updated September 27, 2026