Coding
Like-for-like- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- Qwen3.7 Max
- 49.5
- Supported · #77/183
- Basis
- BenchAlign lane · 4 vs 10 public rows
- Reading
- Qwen3.7 Max leads · intervals overlap
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.7 Max has the higher public score estimate, 68.56 versus 60.5, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Code generation, repair, and software-engineering tasks
Qwen3.7 Max
Qwen3.7 Max leads on the public coding lane, 49.5 to 43.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.5 Flash-Lite is scored on Estimated evidence for agentic, so the reading is 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.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row shows the public-lane category score for both models: the BenchAlign 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.
| Category | Gemini 3.5 Flash-Lite | Qwen3.7 Max | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.6Supported · #121/183 | 49.5Supported · #77/183 | Like-for-likeBenchAlign lane · 4 vs 10 public rows | Qwen3.7 Max leads · intervals overlap |
| Knowledge | 53.0Supported · #71/181 | 62.4Supported · #28/181 | Like-for-likeBenchAlign lane · 2 vs 9 public rows | Qwen3.7 Max leads · intervals overlap |
| Agentic | 43.4Estimated · #104/151 | 42.8Supported · #110/151 | Directional onlyBenchAlign lane · 3 vs 10 public rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | 74.8Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 82.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | 100.0#1/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 76.1#16/48 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 91.1#16/120 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
MRCRv2
Reasoning
Terminal-Bench 2.0
Agentic
LiveCodeBench (Vals)
Coding
SWE-bench Pro
Coding
MMLU-Pro (Vals)
Knowledge
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 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.7 Max has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Qwen3.7 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 3.5 Flash-Lite
Qwen3.7 Max
1M
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationQwen3.7 Max
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingQwen3.7 Max
No comparable hosted API rate
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationQwen3.7 Max
Not sourced
Gemini 3.5 Flash-Lite
Qwen3.7 Max
Not sourced
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideQwen3.7 Max
Not sourced
Gemini 3.5 Flash-Lite
Reasoning
Qwen3.7 Max
Reasoning
Gemini 3.5 Flash-Lite
Proprietary
Qwen3.7 Max
Proprietary
Gemini 3.5 Flash-Lite
Proprietary
Qwen3.7 Max
Proprietary
Gemini 3.5 Flash-Lite
2026-07-21
Qwen3.7 Max
2026-05-16
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
Qwen3.7 Max leads this result
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.7 Max leads this result
QwenClawBench
Not directly comparable
Claw-Eval
Not directly comparable
BFCL v4
Not directly comparable
MCP Atlas
Not directly comparable
VITA-Bench
Not directly comparable
HLE w/ tools
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.0
Qwen3.7 Max leads this result
SWE-bench Pro
Qwen3.7 Max leads this result
LiveCodeBench (Vals)
Qwen3.7 Max leads this result
SWE-bench (Vals)
Gemini 3.5 Flash-Lite leads this result
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
SciCode
Not directly comparable
LiveCodeBench
Not directly comparable
OpenHarmony Bench
Not directly comparable
GPQA Diamond (Vals)
Qwen3.7 Max leads this result
MMLU-Pro (Vals)
Qwen3.7 Max leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Redux
Not directly comparable
SuperGPQA
Not directly comparable
MMMLU
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
Qwen3.7 Max has the higher public score estimate, 68.56 versus 60.5, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Qwen3.7 Max leads the public coding lane, 49.5 to 43.6, with Supported evidence for both models, although the 90% intervals overlap.
Gemini 3.5 Flash-Lite scores higher for agentic tasks on the public lane, 43.4 to 42.8. Gemini 3.5 Flash-Lite 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.
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 September 4, 2026
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