Coding
Like-for-like- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- GPT-5.6 Sol
- 74.4
- Supported · #5/183
- Basis
- BenchAlign lane · 4 vs 11 public rows
- Reading
- GPT-5.6 Sol leads
Gemini 3.5 Flash-Lite vs GPT-5.6 Sol
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.6 Sol has the higher public score, 79.65 versus 60.5, and the 90% score intervals do not overlap.
8 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
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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Sol
GPT-5.6 Sol leads on the public coding lane, 74.4 to 43.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
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
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Gemini 3.5 Flash-Lite only
8 Shared
27 GPT-5.6 Sol only
- Shared results
- 8
- Gemini 3.5 Flash-Lite only
- 2
- GPT-5.6 Sol only
- 27
- Like-for-like categories
- 2 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Knowledge
Like-for-like- Gemini 3.5 Flash-Lite
- 53.0
- Supported · #71/181
- GPT-5.6 Sol
- 80.4
- Supported · #5/181
- Basis
- BenchAlign lane · 2 vs 8 public rows
- Reading
- GPT-5.6 Sol leads
Agentic
Directional only- Gemini 3.5 Flash-Lite
- 43.4
- Estimated · #104/151
- GPT-5.6 Sol
- 70.1
- Supported · #6/151
- Basis
- BenchAlign lane · 3 vs 8 public rows
- Reading
- Directional only
Multimodal
Directional only- Gemini 3.5 Flash-Lite
- 76.1
- #16/48
- GPT-5.6 Sol
- 86.4
- #4/48
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.5 Flash-Lite
- 60.8
- Unranked · 3 rankable rows
- GPT-5.6 Sol
- 69.8
- #14/22
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GPT-5.6 Sol
- 97.0
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GPT-5.6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GPT-5.6 Sol
- 88.8
- #27/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash-Lite | GPT-5.6 Sol | Basis | Reading |
|---|---|---|---|---|
| Coding | 43.6Supported · #121/183 | 74.4Supported · #5/183 | Like-for-likeBenchAlign lane · 4 vs 11 public rows | GPT-5.6 Sol leads |
| Knowledge | 53.0Supported · #71/181 | 80.4Supported · #5/181 | Like-for-likeBenchAlign lane · 2 vs 8 public rows | GPT-5.6 Sol leads |
| Agentic | 43.4Estimated · #104/151 | 70.1Supported · #6/151 | Directional onlyBenchAlign lane · 3 vs 8 public rows | Directional only |
| Multimodal | 76.1#16/48 | 86.4#4/48 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | 69.8#14/22 | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
| Math | Not ranked | 97.0Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 88.8#27/120 | 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) 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.
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
Gemini 3.5 Flash-Lite: 54%GPT-5.6 Sol: 91.9%Normalized gap 37.9Gemini 3.5 Flash-Lite source GPT-5.6 Sol sourceSWE-bench Pro
Coding
Gemini 3.5 Flash-Lite: 54.2%GPT-5.6 Sol: 64.6%Normalized gap 10.4Gemini 3.5 Flash-Lite source GPT-5.6 Sol sourceLiveCodeBench (Vals)
Coding
Gemini 3.5 Flash-Lite: 79.0%GPT-5.6 Sol: 82.6%Normalized gap 3.6Gemini 3.5 Flash-Lite source GPT-5.6 Sol sourceMMLU-Pro (Vals)
Knowledge
Gemini 3.5 Flash-Lite: 85.8%GPT-5.6 Sol: 89.1%Normalized gap 3.3Gemini 3.5 Flash-Lite source GPT-5.6 Sol source
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
- Gemini 3.5 Flash-Lite
- $0.00155
- Fits in one request
- GPT-5.6 Sol
- $0.02
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.5 Flash-Lite
- $0.0225
- Fits in one request
- GPT-5.6 Sol
- $0.34
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.5 Flash-Lite
- $0.037
- Fits in one request
- GPT-5.6 Sol
- $0.5
- Fits in one request
Gemini 3.5 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.5 Flash-LiteGPT-5.6 Sol
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash-Lite
GPT-5.6 Sol
1.05M
OpenAI model catalogAPI model ID
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationGPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogCached-input rate
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 pricingGPT-5.6 Sol
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationGPT-5.6 Sol
text, image
OpenAI model catalogDocumented outputs
Gemini 3.5 Flash-Lite
GPT-5.6 Sol
Provider availability
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideGPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemini 3.5 Flash-Lite
Reasoning
GPT-5.6 Sol
Reasoning
Weight access
Gemini 3.5 Flash-Lite
Proprietary
GPT-5.6 Sol
Proprietary
License
Gemini 3.5 Flash-Lite
Proprietary
GPT-5.6 Sol
Proprietary
Release date
Gemini 3.5 Flash-Lite
2026-07-21
GPT-5.6 Sol
2026-07-09
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, 79.65 versus 60.5, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0225 vs $0.34. Cache-heavy agent loop: $0.037 vs $0.5.
- 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 evidence37 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54%SourceGPT-5.6 Sol91.9%SourceGPT-5.6 Sol leads this result
OSWorld-Verified
Gemini 3.5 Flash-Lite74%SourceGPT-5.6 Sol—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%SourceGPT-5.6 Sol85.8%SourceGPT-5.6 Sol leads this result
Terminal-Bench 3.0
Gemini 3.5 Flash-Lite—GPT-5.6 Sol34.6%SourceNot directly comparable
BrowseComp
Gemini 3.5 Flash-Lite—GPT-5.6 Sol92.2%SourceNot directly comparable
OSWorld 2.0
Gemini 3.5 Flash-Lite—GPT-5.6 Sol62.6%SourceNot directly comparable
CyberGym
Gemini 3.5 Flash-Lite—GPT-5.6 Sol84.5%SourceNot directly comparable
ExploitGym
Gemini 3.5 Flash-Lite—GPT-5.6 Sol33.7%SourceNot directly comparable
Toolathlon
Gemini 3.5 Flash-Lite—GPT-5.6 Sol58%SourceNot directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%SourceGPT-5.6 Sol91.9%SourceGPT-5.6 Sol leads this result
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%SourceGPT-5.6 Sol64.6%SourceGPT-5.6 Sol leads this result
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%SourceGPT-5.6 Sol82.6%SourceGPT-5.6 Sol leads this result
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%SourceGPT-5.6 Sol96.2%SourceGPT-5.6 Sol leads this result
Bug Hunt Bench
Gemini 3.5 Flash-Lite—GPT-5.6 Sol42 fixesSourceNot directly comparable
deepSwe
Gemini 3.5 Flash-Lite—GPT-5.6 Sol72.7%SourceNot directly comparable
FrontierCode 1.1 Extended
Gemini 3.5 Flash-Lite—GPT-5.6 Sol60.6%SourceNot directly comparable
FrontierSWE v2
Gemini 3.5 Flash-Lite—GPT-5.6 Sol32.2%SourceNot directly comparable
cursorBench32
Gemini 3.5 Flash-Lite—GPT-5.6 Sol67.2%SourceNot directly comparable
VulcanBench v3
Gemini 3.5 Flash-Lite—GPT-5.6 Sol87.0%SourceNot directly comparable
VulcanBench CII v1
Gemini 3.5 Flash-Lite—GPT-5.6 Sol86.5%SourceNot directly comparable
Reasoning
MRCRv2
Gemini 3.5 Flash-Lite72.2%SourceGPT-5.6 Sol—Not directly comparable
ARC-AGI-2
Gemini 3.5 Flash-Lite—GPT-5.6 Sol92.5%SourceNot directly comparable
ARC-AGI-3
Gemini 3.5 Flash-Lite—GPT-5.6 Sol7.8%SourceNot directly comparable
GeneBench-Pro
Gemini 3.5 Flash-Lite—GPT-5.6 Sol28.7%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%SourceGPT-5.6 Sol95.2%SourceGPT-5.6 Sol leads this result
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%SourceGPT-5.6 Sol89.1%SourceGPT-5.6 Sol leads this result
GPQA
Gemini 3.5 Flash-Lite—GPT-5.6 Sol94.6%SourceNot directly comparable
GPQA-D
Gemini 3.5 Flash-Lite—GPT-5.6 Sol94.6%SourceNot directly comparable
HLE-Verified
Gemini 3.5 Flash-Lite—GPT-5.6 Sol54.5%SourceNot directly comparable
LABBench2
Gemini 3.5 Flash-Lite—GPT-5.6 Sol82.1%SourceNot directly comparable
HealthBench Professional
Gemini 3.5 Flash-Lite—GPT-5.6 Sol60.5%SourceNot directly comparable
HealthBench Hard
Gemini 3.5 Flash-Lite—GPT-5.6 Sol33.1%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, Gemini 3.5 Flash-Lite or GPT-5.6 Sol?
GPT-5.6 Sol has the higher public score, 79.65 versus 60.5, 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, Gemini 3.5 Flash-Lite or GPT-5.6 Sol?
GPT-5.6 Sol leads the public coding lane, 74.4 to 43.6, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GPT-5.6 Sol?
GPT-5.6 Sol scores higher for agentic tasks on the public lane, 70.1 to 43.4. 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.
Which costs less, Gemini 3.5 Flash-Lite or GPT-5.6 Sol?
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.02 on GPT-5.6 Sol; repository review costs $0.0225 and $0.34; the cache-heavy agent loop costs $0.037 and $0.5. Costs use the listed standard API rates.
Which has the larger context window, Gemini 3.5 Flash-Lite or GPT-5.6 Sol?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated September 4, 2026
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