Updated October 17, 2026. Rank says Gemini 3.8 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Gemini 3.8 Flash has the higher public score estimate, 69.25 versus 64.35, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 17 results are shared. Category rows resting on Estimated evidence or 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.
Code generation, repair, and software-engineering tasks
GPT-5.6 Luna
GPT-5.6 Luna has the higher public coding point estimate, 61.6 to 61, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Gemini 3.8 Flash
Gemini 3.8 Flash has the higher public agentic point estimate, 65.9 to 56, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
1K fresh input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GPT-5.6 Luna has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
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.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 36.5ARC-AGI-2Reasoning
Normalized gap 29.7OSWorld 2.0Agentic
Normalized gap 13.4ARC-AGI-3Reasoning
Normalized gap 10.2FrontierMath v2 (Tiers 1-3)Math
Normalized gap 10.2Each row shows the public-lane category score for both models: the BenchAlign v5.8 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 3.8 Flash | GPT-5.6 Luna | Basis | Reading |
|---|---|---|---|---|
| Agentic | 65.9Supported · #16/128 | 56.0Supported · #39/128 | Like-for-likeBenchAlign v5.8 lane · 9 vs 10 public rows | Gemini 3.8 Flash leads · intervals overlap |
| Coding | 61.0Supported · #17/150 | 61.6Supported · #16/150 | Like-for-likeBenchAlign v5.8 lane · 8 vs 8 public rows | GPT-5.6 Luna leads · intervals overlap |
| Reasoning | 74.3#17/28 | 58.2#27/28 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Gemini 3.8 Flash leads |
| Knowledge | 73.5Supported · #13/184 | 64.8Supported · #37/184 | Like-for-likeBenchAlign v5.8 lane · 6 vs 6 public rows | Gemini 3.8 Flash leads · intervals overlap |
| Multimodal | 87.3#9/54 | 79.6#19/54 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 69.7Unranked · 2 rankable rows | 84.0Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
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 3.8 Flash
GPT-5.6 Luna
1.05M
OpenAI model catalogGemini 3.8 Flash
gemini-3.8-flash
Google Gemini 3.8 Flash API documentationGPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.8 Flash
$0.075 per 1M cached input tokens
Google Gemini API pricingGPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingGemini 3.8 Flash
text, image, video, audio, pdf
Google Gemini 3.8 Flash API documentationGPT-5.6 Luna
text, image
OpenAI model catalogGemini 3.8 Flash
GPT-5.6 Luna
Gemini 3.8 Flash
Generally Available · Gemini API, Google AI Studio, Android Studio, Stitch, Gemini app, Gemini Enterprise, Google AI Mode, Google Sheets, Google Antigravity
Google Gemini 3.8 Flash and Gemini 3.8 Flash Cyber launchGPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogGemini 3.8 Flash
Reasoning
GPT-5.6 Luna
Reasoning
Gemini 3.8 Flash
Proprietary
GPT-5.6 Luna
Proprietary
Gemini 3.8 Flash
Proprietary
GPT-5.6 Luna
Proprietary
Gemini 3.8 Flash
2026-09-02
GPT-5.6 Luna
2026-07-09
Gemini 3.8 Flash has the higher public score estimate, 69.25 versus 64.35, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GPT-5.6 Luna has the higher public coding point estimate, 61.6 to 61, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemini 3.8 Flash has the higher public agentic tasks point estimate, 65.9 to 56, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00263 on Gemini 3.8 Flash and $0.0008 on GPT-5.6 Luna; repository review costs $0.04875 and $0.0136; the cache-heavy agent loop costs $0.0675 and $0.02. Costs use the listed standard API rates.
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Finance Agent v2
Not directly comparable
Terminal-Bench 2.1
Gemini 3.8 Flash leads this result
Terminal-Bench 4.0
Not directly comparable
OSWorld 2.0
Gemini 3.8 Flash leads this result
Terminal-Bench 2.1 (Vals)
Gemini 3.8 Flash leads this result
ApprenticeBench
Shared sourceGemini 3.8 Flash leads this result
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
MazeBench
Gemini 3.8 Flash leads this result
Terminal-Bench 3.0
Not directly comparable
BrowseComp
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
Toolathlon
Not directly comparable
DeepSWE
Gemini 3.8 Flash leads this result
Terminal-Bench 2.1
Gemini 3.8 Flash leads this result
CursorBench 3.2
Shared sourceGemini 3.8 Flash leads this result
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna leads this result
FrontierSWE v2
Not directly comparable
CursorBench 4.0
Shared sourceGemini 3.8 Flash leads this result
Bug Hunt Bench
Not directly comparable
SWE-bench Pro
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
VulcanBench v3
Not directly comparable
CharXiv w/o tools
Not directly comparable
LVBench
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
BioMysteryBench (human-solvable)
Not directly comparable
BioMysteryBench (human-difficult)
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.8 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.8 Flash leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna leads this result
FrontierMath v2 (Tier 4)
GPT-5.6 Luna leads this result
FrontierMath (legacy)
Not directly comparable
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Last updated October 17, 2026
