Multimodal
Like-for-like- Qwen3.7 Plus
- 81.5
- Qwen3.8 Max
- 86.3
- Weighted basis
- 2 vs 2 rows
- Reading
- Qwen3.8 Max leads
Model comparison
Updated August 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Qwen3.7 Plus has the higher public score estimate, 66 versus 65.4, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
23 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
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
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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
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
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | Qwen3.7 Plus | Qwen3.8 Max | Weighted basis | Reading |
|---|---|---|---|---|
| Multimodal | 81.5 | 86.3 | Like-for-like2 vs 2 rows | Qwen3.8 Max leads |
| Agentic | 71.7 | 86.1 | Directional only2 vs 1 rows | Directional only |
| Coding | 75.6 | 67.7 | Directional only4 vs 1 rows | Directional only |
| Reasoning | 91.7 | 78.3 | Directional only1 vs 2 rows | Directional only |
| Knowledge | 60.1 | 50.2 | Directional only4 vs 2 rows | Directional only |
| Instruction following | 84.5 | 82.8 | Directional only2 vs 1 rows | Directional only |
| Math | 92.9 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | 85.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
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.
OSWorld-Verified
Agentic
SWE-bench Pro
Coding
HLE
Knowledge
CharXiv
Multimodal
IFBench
Instruction following
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.
1K fresh input + 500 output tokens
Qwen3.7 Plus has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.7 Plus has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Qwen3.7 Plus has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Qwen3.7 Plus
1M
Qwen3.8 Max
Qwen3.7 Plus
Not sourced
Qwen3.8 Max
qwen3.8-max
Alibaba Cloud Model Studio pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Qwen3.7 Plus
No comparable hosted API rate
Qwen3.8 Max
No comparable hosted API rate
Alibaba Cloud Model Studio pricingQwen3.7 Plus
Not sourced
Qwen3.8 Max
Not sourced
Qwen3.7 Plus
Not sourced
Qwen3.8 Max
Not sourced
Qwen3.7 Plus
Not sourced
Qwen3.8 Max
Not sourced
Qwen3.7 Plus
Reasoning
Qwen3.8 Max
Reasoning
Qwen3.7 Plus
Proprietary
Qwen3.8 Max
Proprietary
Qwen3.7 Plus
Proprietary
Qwen3.8 Max
Proprietary
Qwen3.7 Plus
2026-06-03
Qwen3.8 Max
2026-08-03
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
QwenClawBench
Not directly comparable
Claw-Eval
Not directly comparable
BFCL v4
Not directly comparable
MCP Atlas
Not directly comparable
VITA-Bench
Not directly comparable
DeepPlanning
Not directly comparable
OSWorld-Verified
Qwen3.8 Max leads this result
AndroidWorld
Qwen3.8 Max leads this result
OSWorld 2.0
Qwen3.8 Max leads this result
Terminal-Bench 2.1
Not directly comparable
CoWorkBench
Not directly comparable
JobBench
Not directly comparable
skillsBench
Not directly comparable
Agents' Last Exam
Not directly comparable
AutomationBench
Not directly comparable
Toolathlon-Verified
Not directly comparable
WideResearch
Not directly comparable
HLE w/ tools
Not directly comparable
WebArena-Verified
Not directly comparable
MobileWorld
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Qwen3.8 Max leads this result
SWE Multilingual
Not directly comparable
NL2Repo
Qwen3.8 Max leads this result
SciCode
Not directly comparable
LiveCodeBench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
deepSwe
Not directly comparable
FrontierSWE
Not directly comparable
MLS-Bench Lite
Not directly comparable
PaperBench
Not directly comparable
GPQA
Qwen3.8 Max leads this result
GPQA-D
Qwen3.8 Max leads this result
HLE
Qwen3.8 Max leads this result
MMLU-Pro
Not directly comparable
MMLU-Redux
Not directly comparable
SuperGPQA
Not directly comparable
MMMLU
Not directly comparable
HLE w/o tools
Not directly comparable
MMLU-ProX
Not directly comparable
NOVA-63
Not directly comparable
INCLUDE
Not directly comparable
MAXIFE
Not directly comparable
PolyMath
Not directly comparable
MMMU-Pro
Qwen3.8 Max leads this result
MathVision
Qwen3.8 Max leads this result
CharXiv
Qwen3.8 Max leads this result
ERQA
Qwen3.8 Max leads this result
MedXpertQA (MM)
Qwen3.8 Max leads this result
ScreenSpot Pro
Qwen3.8 Max leads this result
SimpleVQA
Qwen3.7 Plus leads this result
MMSearch-Plus
Not directly comparable
RealWorldQA
Qwen3.8 Max leads this result
OmniDocBench 1.5
Qwen3.8 Max leads this result
OCRBench V2
Qwen3.8 Max leads this result
ODINW13
Not directly comparable
Video-MME (with subtitle)
Qwen3.8 Max leads this result
VideoMMMU
Qwen3.8 Max leads this result
MLVU (M-Avg)
Qwen3.8 Max leads this result
MathVision w/ Python
Not directly comparable
BabyVision
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench
Not directly comparable
ZeroBench w/ Python
Not directly comparable
Vision2Web
Not directly comparable
CharXiv w/o tools
Not directly comparable
CC-OCR
Not directly comparable
PerceptionBench
Not directly comparable
MMVU
Not directly comparable
LVBench
Not directly comparable
Qwen3.7 Plus has the higher public score estimate, 66 versus 65.4, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
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.
The current agentic tasks 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
Last updated August 3, 2026
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