Multimodal
Directional only- Gemini 3.1 Flash-Lite
- 73.2
- Qwen3.8 Max
- 86.3
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Model comparison
Updated August 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Qwen3.8 Max has the higher public score estimate, 65.4 versus 50.1, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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.
1 category uses 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 | Gemini 3.1 Flash-Lite | Qwen3.8 Max | Weighted basis | Reading |
|---|---|---|---|---|
| Multimodal | 73.2 | 86.3 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | 86.1 | Not comparable0 vs 1 rows | Not comparable |
| Coding | Not measured | 67.7 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | 78.3 | Not comparable0 vs 2 rows | Not comparable |
| Knowledge | Not measured | 50.2 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | 82.8 | Not comparable0 vs 1 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.
CharXiv
Multimodal
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
Qwen3.8 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.8 Max has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
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.
Gemini 3.1 Flash-Lite
Qwen3.8 Max
Gemini 3.1 Flash-Lite
gemini-3.1-flash-lite
Google Gemini API pricingQwen3.8 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.
Gemini 3.1 Flash-Lite
$0.025 per 1M cached input tokens
Google Gemini API pricingQwen3.8 Max
No comparable hosted API rate
Alibaba Cloud Model Studio pricingGemini 3.1 Flash-Lite
Not sourced
Qwen3.8 Max
Not sourced
Gemini 3.1 Flash-Lite
Not sourced
Qwen3.8 Max
Not sourced
Gemini 3.1 Flash-Lite
Not sourced
Qwen3.8 Max
Not sourced
Gemini 3.1 Flash-Lite
Non-Reasoning
Qwen3.8 Max
Reasoning
Gemini 3.1 Flash-Lite
Proprietary
Qwen3.8 Max
Proprietary
Gemini 3.1 Flash-Lite
Proprietary
Qwen3.8 Max
Proprietary
Gemini 3.1 Flash-Lite
2026-03-03
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.
Gert Labs
Not directly comparable
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
OSWorld-Verified
Not directly comparable
OSWorld 2.0
Not directly comparable
WebArena-Verified
Not directly comparable
AndroidWorld
Not directly comparable
MobileWorld
Not directly comparable
Vibe Code Bench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Not directly comparable
deepSwe
Not directly comparable
NL2Repo
Not directly comparable
FrontierSWE
Not directly comparable
MLS-Bench Lite
Not directly comparable
PaperBench
Not directly comparable
CharXiv
Qwen3.8 Max leads this result
MMMU-Pro
Not directly comparable
MathVision
Not directly comparable
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
MedXpertQA (MM)
Not directly comparable
ScreenSpot Pro
Not directly comparable
Vision2Web
Not directly comparable
CharXiv w/o tools
Not directly comparable
OmniDocBench 1.5
Not directly comparable
OCRBench V2
Not directly comparable
CC-OCR
Not directly comparable
RealWorldQA
Not directly comparable
ERQA
Not directly comparable
SimpleVQA
Not directly comparable
PerceptionBench
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
VideoMMMU
Not directly comparable
MMVU
Not directly comparable
MLVU (M-Avg)
Not directly comparable
LVBench
Not directly comparable
IFBench
Not directly comparable
Qwen3.8 Max has the higher public score estimate, 65.4 versus 50.1, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
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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