Agentic
Like-for-like- GPT-5.6 Sol
- 92.0
- Qwen3.5 397B
- 56.5
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.6 Sol leads
Model comparison
GPT-5.6 Sol vs Qwen3.5 397B
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Sol has the higher public score, 81.48 versus 56.24, and the 90% score intervals do not overlap.
6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the same 2 weighted benchmark rows.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
18 GPT-5.6 Sol only
6 Shared
32 Qwen3.5 397B only
- Shared results
- 6
- GPT-5.6 Sol only
- 18
- Qwen3.5 397B only
- 32
- Like-for-like categories
- 1 / 8
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- GPT-5.6 Sol
- 64.6
- Qwen3.5 397B
- 66.5
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.6 Sol
- 94.6
- Qwen3.5 397B
- 56.6
- Weighted basis
- 1 vs 4 rows
- Reading
- Directional only
Multimodal
Directional only- GPT-5.6 Sol
- 83.0
- Qwen3.5 397B
- 79.6
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Sol
- 92.5
- Qwen3.5 397B
- 63.2
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 87.5
- Qwen3.5 397B
- 90.6
- Weighted basis
- 2 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not measured
- Qwen3.5 397B
- 84.7
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Sol
- Not measured
- Qwen3.5 397B
- 92.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Qwen3.5 397B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 92.0 | 56.5 | Like-for-like2 vs 2 rows | GPT-5.6 Sol leads |
| Coding | 64.6 | 66.5 | Directional only1 vs 2 rows | Directional only |
| Knowledge | 94.6 | 56.6 | Directional only1 vs 4 rows | Directional only |
| Multimodal | 83.0 | 79.6 | Directional only1 vs 2 rows | Directional only |
| Reasoning | 92.5 | 63.2 | Not comparable1 vs 1 rows | Not comparable |
| Math | 87.5 | 90.6 | Not comparable2 vs 2 rows | Not comparable |
| Multilingual | Not measured | 84.7 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | Not measured | 92.6 | Not comparable0 vs 1 rows | Not comparable |
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
BrowseComp
Agentic
SWE-bench Pro
Coding
GPQA
Knowledge
MMMU-Pro
Multimodal
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-5.6 Sol
- $0.02
- 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
- GPT-5.6 Sol
- $0.34
- 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
- GPT-5.6 Sol
- $0.5
- Fits in one request
- Qwen3.5 397B
- $0.168
- Does not fit in one request
- Cached input priced at the published list-input rate
Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 SolQwen3.5 397B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogQwen3.5 397B
128K
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogQwen3.5 397B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingQwen3.5 397B
Not published
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogQwen3.5 397B
Not sourced
Documented outputs
GPT-5.6 Sol
Qwen3.5 397B
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogQwen3.5 397B
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Qwen3.5 397B
Non-Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Qwen3.5 397B
Open Weight
License
GPT-5.6 Sol
Proprietary
Qwen3.5 397B
Open Weight
Release date
GPT-5.6 Sol
2026-07-09
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
- GPT-5.6 Sol has the higher public score, 81.48 versus 56.24, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.34 vs $0.0408. Cache-heavy agent loop: $0.5 vs $0.168.
- Context tradeoff
- GPT-5.6 Sol has the larger documented window (1.05M).
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 evidence56 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceQwen3.5 397B52.5%SourceGPT-5.6 Sol leads this result
BrowseComp
GPT-5.6 Sol92.2%SourceQwen3.5 397B62%SourceGPT-5.6 Sol leads this result
OSWorld 2.0
GPT-5.6 Sol62.6%SourceQwen3.5 397B—Not directly comparable
CyberGym
GPT-5.6 Sol84.5%SourceQwen3.5 397B—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceQwen3.5 397B—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceQwen3.5 397B36.3%SourceGPT-5.6 Sol leads this result
Claw-Eval
GPT-5.6 Sol—Qwen3.5 397B56.8%SourceNot directly comparable
QwenClawBench
GPT-5.6 Sol—Qwen3.5 397B51.8%SourceNot directly comparable
τ³-bench results
GPT-5.6 Sol—Qwen3.5 397B68.4%SourceNot directly comparable
VITA-Bench
GPT-5.6 Sol—Qwen3.5 397B43.7%SourceNot directly comparable
DeepPlanning
GPT-5.6 Sol—Qwen3.5 397B37.6%SourceNot directly comparable
MCP Atlas
GPT-5.6 Sol—Qwen3.5 397B46.1%SourceNot directly comparable
MCP-Tasks
GPT-5.6 Sol—Qwen3.5 397B74.2%SourceNot directly comparable
WideResearch
GPT-5.6 Sol—Qwen3.5 397B74.0%SourceNot directly comparable
Gert Labs
GPT-5.6 Sol—Qwen3.5 397B46.76%SourceNot directly comparable
ResearchClawBench
GPT-5.6 Sol—Qwen3.5 397B14.2%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Sol64.6%SourceQwen3.5 397B50.9%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.0
GPT-5.6 Sol91.9%SourceQwen3.5 397B—Not directly comparable
deepSwe
GPT-5.6 Sol72.7%SourceQwen3.5 397B—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceQwen3.5 397B—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceQwen3.5 397B—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceQwen3.5 397B—Not directly comparable
SWE-bench Verified
GPT-5.6 Sol—Qwen3.5 397B76.2%SourceNot directly comparable
LiveCodeBench v6
GPT-5.6 Sol—Qwen3.5 397B83.6%SourceNot directly comparable
Reasoning
ARC-AGI-2
GPT-5.6 Sol92.5%SourceQwen3.5 397B—Not directly comparable
ARC-AGI-3
GPT-5.6 Sol7.8%SourceQwen3.5 397B—Not directly comparable
GeneBench-Pro
GPT-5.6 Sol28.7%SourceQwen3.5 397B—Not directly comparable
LongBench v2
GPT-5.6 Sol—Qwen3.5 397B63.2%SourceNot directly comparable
AI-Needle
GPT-5.6 Sol—Qwen3.5 397B68.7%SourceNot directly comparable
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceQwen3.5 397B88.4%SourceGPT-5.6 Sol leads this result
GPQA-D
GPT-5.6 Sol94.6%SourceQwen3.5 397B—Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%SourceQwen3.5 397B—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceQwen3.5 397B—Not directly comparable
SuperGPQA
GPT-5.6 Sol—Qwen3.5 397B70.4%SourceNot directly comparable
MMLU-Pro
GPT-5.6 Sol—Qwen3.5 397B87.8%SourceNot directly comparable
MMLU-Redux
GPT-5.6 Sol—Qwen3.5 397B94.9%SourceNot directly comparable
C-Eval
GPT-5.6 Sol—Qwen3.5 397B93%SourceNot directly comparable
HLE
GPT-5.6 Sol—Qwen3.5 397B28.7%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.6 Sol89%SourceQwen3.5 397B—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Sol89.000%SourceQwen3.5 397B—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Sol83.000%SourceQwen3.5 397B—Not directly comparable
AIME26
GPT-5.6 Sol—Qwen3.5 397B93.3%SourceNot directly comparable
HMMT Feb 2025
GPT-5.6 Sol—Qwen3.5 397B94.8%SourceNot directly comparable
HMMT Nov 2025
GPT-5.6 Sol—Qwen3.5 397B92.7%SourceNot directly comparable
HMMT Feb 2026
GPT-5.6 Sol—Qwen3.5 397B87.9%SourceNot directly comparable
MMAnswerBench
GPT-5.6 Sol—Qwen3.5 397B80.9%SourceNot directly comparable
Multilingual
Multimodal
MMMU-Pro
GPT-5.6 Sol83%SourceQwen3.5 397B79%SourceGPT-5.6 Sol leads this result
MMMU-Pro w/ Python
GPT-5.6 Sol84.6%SourceQwen3.5 397B—Not directly comparable
MathVision
GPT-5.6 Sol—Qwen3.5 397B88.6%SourceNot directly comparable
CharXiv
GPT-5.6 Sol—Qwen3.5 397B80.8%SourceNot directly comparable
VideoMMMU
GPT-5.6 Sol—Qwen3.5 397B84.7%SourceNot directly comparable
ScreenSpot Pro
GPT-5.6 Sol—Qwen3.5 397B65.6%SourceNot directly comparable
V*
GPT-5.6 Sol—Qwen3.5 397B95.8%SourceNot directly comparable
Instruction following
IFEval
GPT-5.6 Sol—Qwen3.5 397B92.6%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Sol or Qwen3.5 397B?
GPT-5.6 Sol has the higher public score, 81.48 versus 56.24, 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-5.6 Sol or Qwen3.5 397B?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.6 Sol or Qwen3.5 397B?
GPT-5.6 Sol leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Which costs less, GPT-5.6 Sol or Qwen3.5 397B?
For the stated presets, chat costs $0.02 on GPT-5.6 Sol and $0.0024 on Qwen3.5 397B; repository review costs $0.34 and $0.0408; the cache-heavy agent loop costs $0.5 and $0.168. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.6 Sol or Qwen3.5 397B?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 128K.
Related comparisons
Last updated August 10, 2026
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