Test Gemini 3.8 Flash
Text, image, audio, video and PDF inputs; Google Search/Maps grounding; lower paid Standard token price at the stated dates.
Gemini model specifications ↗MODEL COMPARISON / VERIFIED 2026-11-15
GPT-6 Sol vs Gemini 3.8 Flash
API Pricing, Coding & Which to Choose
A source-first comparison for developers choosing between OpenAI and Google for coding, agents and production AI. Specs, token economics, long context, multimodal inputs and tool ecosystems are separated from vendor performance claims.
DIRECT ANSWER · GPT-6 SOL VS GEMINI 3.8 FLASH
GPT-6 Sol vs Gemini 3.8 Flash: which AI model should you choose?
Choose Gemini 3.8 Flash as a first test when low Standard text-token cost, PDF/video/audio input and Google's tools matter most. Choose GPT-6 Sol when you need OpenAI's Responses API workflow, more reasoning-effort settings or up to 128,000 output tokens. Both support million-token-scale input; neither is universally better at coding, reasoning or agents.
These are specific API model IDs (gpt-6-sol and gemini-3.8-flash), not a comparison of the consumer ChatGPT and Gemini apps. The newer GPT-6.1 Sol is a separate model: this comparison deliberately retains the 6 Sol endpoint for people searching that exact matchup.
Test GPT-6 Sol
Responses API tools, granular effort controls and a 128K maximum output limit. Recheck long-context pricing beyond 272K input tokens.
GPT-6 Sol model specifications ↗Run the same tasks on both
Record passed tests, human corrections, billable tokens, tool charges and retries. A lower per-token price need not mean lower cost per successful task.
SXF comparison checklist ↗Source check: November 11, 2026. Prices and API limits are vendor documentation; benchmark observations are external and versioned. Evidence and limitations.
LIVE VERIFIED FACTS
Current facts from the SXF model database.
GPT-6 Sol
- Context
- 1,050,000
- Max output
- 128,000
- Input / MTok
- $2.00
- Cached / MTok
- $0.20
- Output / MTok
- $10.00
- Input types
- text + image
Gemini 3.8 Flash
- Context
- 1,048,576
- Max output
- 65,536
- Input / MTok
- $0.75
- Cached / MTok
- $0.075
- Output / MTok
- $3.75
- Input types
- text + image + video + audio + PDF
Facts are generated from the canonical model catalog, not copied into this comparison.Current data ↗Change ledger ↗
INDEPENDENT EVALUATIONS
Comparable evidence, configuration attached.
ComparableArtificial Analysis
Artificial Analysis Intelligence Index 4.3.2
GPT-6 Sol: 42 · Gemini 3.8 Flash: 41
Group: artificial-analysis-v4.3.2A higher score is meaningful only within the exact comparable group shown. Under-review benchmarks are never used alone for a quality conclusion.
WHAT CHANGED
Changes affecting this comparison.
No post-baseline factual changes recorded for GPT-6 Sol, Gemini 3.8 Flash since 2026-09-27.
HISTORICAL BASELINE2026-09-27Original ledger snapshot, not the latest source check. See Last verified above.
INDEPENDENT EVIDENCE · NOT A UNIVERSAL WINNER
GPT-6 Sol vs Gemini 3.8 Flash benchmarks: what does 42 vs 41 mean?
SXF's versioned registry contains a historical Artificial Analysis Intelligence Index v4.3.2 snapshot. The two recorded results are comparable within that version and configuration, but they are not today's live intelligence ranking, measured application latency or a result generated by SXF.
Artificial Analysis Intelligence Index v4.3.2
Source checkedArtificial Analysis Intelligence Index v4.3.2
Source checkedBoth records use high reasoning/thinking, benchmark-defined tools, and the same comparable group: artificial-analysis-v4.3.2. The observation dates are SXF's source-verification dates, not necessarily benchmark execution dates. A 1-point index difference is not 1% better or a coding win rate.
Independent score vs provider claim
The composite index combines multiple evaluations. Vendor model cards describe different experiments; scores from different test sets, versions or tool configurations are not interchangeable. In particular, the present Artificial Analysis comparison may display newer index versions and unequal effort settings (for example, Max versus High); do not substitute those figures for this archived High-versus-High observation.
Artificial Analysis index methodology ↗Which model is better for coding?
Both providers position their models for complex engineering and agents. Google's Gemini 3.8 model card reports results on its coding benchmarks, while OpenAI documents Sol's coding focus. Without the same task harness and reasoning configuration, it is not valid to declare a cross-provider coding winner from separate vendor scores.
Compare on your repository ↗Data provenance: SXF versioned observations ↗, archived index reference ↗, evaluator's comparison ↗. No synthetic winner score is calculated.
OFFICIAL SPECIFICATIONS
Side-by-side facts.
| Model | Provider | Positioning | Context | Max output | Reasoning | Modalities | Input | Cached | Output |
|---|---|---|---|---|---|---|---|---|---|
| GPT-6 Solgpt-6-sol | OpenAI | Complex coding and agentic workflows | 1,050,000 | 128,000 | none · low · medium · high · xhigh · max | Text + image input · text output | $2.00 | $0.20 | $10.00 |
| Gemini 3.8 Flashgemini-3.8-flash | Long-horizon software engineering, autonomous agents and enterprise workflows | 1,048,576 | 65,536 | low · medium · high | Text + image + video + audio + PDF input · text output | $0.75* | $0.075* | $3.75* |
GPT-6 Sol rates shown are Standard short-context rates. OpenAI applies higher rates above 272K input tokens. Gemini rates marked * are Google's introductory Standard paid prices through December 31, 2026.
PRICING
Gemini 3.8 Flash is cheaper on headline token rates—especially in 2026.
Token price is not task price, but the gap is large enough to matter for routing and high-volume workloads.
100K input + 10K output
GPT-6 Sol$0.3100K input + 10K output
Gemini 3.8 Flash$0.1125100K input + 10K output
10M input + 1M output
GPT-6 Sol$3010M input + 1M output
Gemini 3.8 Flash$11.2510M input + 1M output
500K input + 50K output
GPT-6 Sol$2.75500K input + 50K output
Gemini 3.8 Flash$0.5625500K input + 50K output
Why the 500K example changes
GPT-6 Sol crosses OpenAI’s 272K long-context threshold, so the full request uses $4 input and $15 output per million tokens. Google’s current 3.8 Flash pricing documentation does not list an equivalent 272K multiplier.
Gemini pricing after 2026
Google’s introductory rates expire December 31, 2026. Standard pricing doubles to $1.50 input and $7.50 output per million tokens on January 1, 2027.
Gemini 3.8 Flash at 2027 Standard rates
100K input + 10K output$0.225Gemini from Jan 1, 2027
10M input + 1M output$22.5Gemini from Jan 1, 2027
500K input + 50K output$1.125Gemini from Jan 1, 2027
EXCLUSIVE SXF / AI · COST-TO-QUALITY SCENARIO
SXF Cost-per-Success Lab: GPT-6 Sol vs Gemini 3.8 Flash
Unlike a price-per-million-token table, this calculator estimates the spend required to complete a target number of successful tasks. Enter your own task success rates and token usage, then compare short versus long context and 2026 versus 2027 Gemini pricing. All calculations run locally in this browser.
ESTIMATED COST TO DELIVER THE TARGET
GPT-6 Sol$31.5789
Gemini 3.8 Flash$11.8421
Difference: Sol minus Gemini$19.7368
At these assumptions, each successful task requires 1/0.95 expected attempts. Sol's 272K threshold applies per request, not to monthly token totals.
Illustrative model, not independently measured success rates. Excludes grounding, tool calls, cache storage/writes, audio/video surcharges, regional fees, taxes and model-specific service tiers.
Transparent SXF formula
Expected attempts = target successes ÷ assumed success probability. Each attempt costs (uncached input × input rate + cached input × cached rate + billed output × output rate) ÷ 1,000,000. Sol's entire request uses 2× input/cache and 1.5× output rates if its input exceeds 272,000 tokens. Gemini's introductory Standard rates change January 1, 2027.
A success probability is a modelling assumption, not a guaranteed production property. Compare real costs only after measuring output tokens—including thinking—failures, retries and variable tool use.
CONTEXT & OUTPUT
The context windows are practically the same size; the output ceilings are not.
GPT-6 Sol lists a 1,050,000-token context window. Gemini 3.8 Flash lists 1,048,576 input tokens. The difference is about 0.14% and is unlikely to determine a real architecture by itself.
The more meaningful specification difference is maximum output: GPT-6 Sol supports up to 128,000 output tokens, while Gemini 3.8 Flash lists 65,536. Very few ordinary requests need either limit, but the larger ceiling can matter for code generation, long document creation and extended outputs.
Context capacity should not be treated as a target. Retrieval, caching, context selection and tool access usually matter more than filling a million-token window on every call.
CODING
Both target real software engineering, but the surrounding workflow differs.
Complex coding + agents
OpenAI explicitly describes Sol as built for complex coding and agentic workflows. GPT-6 guidance also adds async tool calling and mid-turn steering for long-running applications.
Long-horizon engineering
Google explicitly targets real-world software engineering, complex multi-file refactoring and deterministic tool execution while retaining Flash-level cost positioning.
Repository fit matters more than a generic score
Use the same repository, tests, tool permissions and acceptance criteria. Measure accepted patches, retries, token use, latency and human correction rather than selecting from one benchmark.
AGENTS & TOOLS
Both are serious agent models with different platform advantages.
GPT-6 SOL · RESPONSES APIOpenAI-native agent stack
Use the Responses API for built-in tools and function calling. OpenAI’s current model ecosystem includes web search, file search, computer use and other hosted tools.
GPT-6 SOL · ASYNC TOOLSContinue while tools run
GPT-6 guidance supports asynchronous tool calling so the model can continue independent reasoning while application tools execute.
GPT-6 SOL · MID-TURN STEERINGUpdate instructions during work
Applications can send new user instructions while GPT-6 is working, useful for correcting or redirecting long agent tasks.
GEMINI · GROUNDINGSearch + Google Maps
Gemini 3.8 Flash supports Google Search grounding and Google Maps grounding for workflows that depend on current web or location evidence.
GEMINI · EXECUTIONCode execution + computer use
Google lists code execution as supported and computer use as preview, alongside function calling, file search and URL context.
GEMINI · MANAGED AGENTSDefault Antigravity model
Google’s latest-model guide says Gemini 3.8 Flash is the default for its managed Antigravity agent and SDK.
REASONING CONTROLS
Sol exposes a wider reasoning range; Gemini keeps the control simpler.
GPT-6 Sol supports none, low, medium, high, xhigh and max reasoning effort, with medium as the default. That range makes it possible to use one model across low-reasoning and deep-reasoning routes while keeping the same model ID.
Gemini 3.8 Flash supports low, medium and high thinking levels, also defaulting to medium. Google notes that the model may deliberately use more thinking tokens on difficult long-running work, making smaller reasoning steps and checking results along the way.
In both ecosystems, the right setting is the lowest level that reliably clears the task’s quality bar. Reasoning controls directly affect latency and output-token cost.
MULTIMODAL
Gemini 3.8 Flash accepts more input types.
Text + image input
OpenAI’s model card lists text input/output and image input. It does not list audio or video as supported model modalities for Sol.
Text + image + video + audio + PDF
Google lists all five input types on one stable model endpoint, which can simplify workflows that reason across media and documents.
Use dedicated live models
Standard Gemini 3.8 Flash does not support the Live API. Google provides separate Gemini 3.8 Live models for low-latency voice experiences.
API & DEPLOYMENT
The platform around the model may decide the choice.
Responses API ecosystem
Sol fits naturally when your application already uses OpenAI Responses, hosted tools, OpenAI SDKs, model routing or other GPT-6 family models.
Gemini API + Google Cloud
3.8 Flash fits naturally when applications need Google Search/Maps grounding, AI Studio development, Vertex AI deployment or Antigravity managed-agent workflows.
Check the processing tier
OpenAI notes that EU data residency for GPT-6 Sol is available only with Standard processing. Google Cloud and Gemini API data-governance choices depend on the selected product and account terms.
WHEN TO CHOOSE
Route by workload instead of declaring one universal winner.
High-volume agent workloads
Its current lower token price is attractive when tasks require real reasoning and tools but traffic volume makes higher token economics difficult.
Rich multimodal input
Choose it when the same workflow must reason over video, audio, PDFs and images without splitting input processing across separate base models.
OpenAI-native agent architecture
Choose Sol when your system depends on Responses API patterns, GPT-6 reasoning controls, mid-turn steering or other OpenAI-native tooling.
Large generated outputs
The 128K max-output ceiling can matter for very large code or document-generation tasks where Gemini’s 65,536-token output limit is constraining.
LIMITS & CAVEATS
What can make a headline comparison misleading.
01Gemini’s 2026 price is promotional.
The current gap narrows on January 1, 2027 when Google doubles 3.8 Flash Standard token rates.
02Sol’s long-context rates change after 272K.
Cost comparisons using short prompts cannot be extrapolated to 500K–1M-token requests without applying OpenAI’s long-context multiplier.
03Tool costs are not included in token examples.
Search, maps, computer use, regional processing and other platform services can change total cost materially.
04Vendor benchmark harnesses differ.
Do not compare isolated scores unless the task definition, tools, reasoning settings, scaffolding and evaluation method are aligned.
05Agent security is outside the base model.
Regardless of model, tool permissions, sandboxing, approvals, secrets handling and auditability must be enforced by the application.
06Product and API experiences are not identical.
A comparison of model APIs does not automatically describe ChatGPT, Gemini app, Workspace, AI Studio or other packaged product behavior.
SXF ORIGINAL DECISION FRAMEWORK
How to compare GPT-6 Sol and Gemini 3.8 Flash on real coding, reasoning and multimodal tasks
A fair comparison is not just model specifications. Keep the prompts, test data, permissions and grading criteria constant. Use the same effort class where possible and measure the result each application actually needs.
| Workload | What to run | Measure | How to decide |
|---|---|---|---|
| Coding agents | Matched repository bugs, tests and tool permissions | Accepted patches, test pass rate, retries, billed tokens, wall-clock time | Cost per passing patch and review burden |
| Reasoning | Verified Q&A and case-specific multi-step questions | Correctness, justified citations, reasoning tokens and latency | Quality at the required budget and effort level |
| Image & PDFs | Same charts, diagrams, scanned files and question set | Extraction accuracy, wrong values, page references and cost | Successful verified document answers |
| Video & audio | Direct multimodal Gemini input; externally prepared text/transcript for Sol | Pipeline complexity, transcript/vision errors, turnaround and added service fees | Compare the whole pipeline; Sol is not native audio/video input |
| Long context | 100K, 272K and 300K+ input token cases | Recall, task success, generated tokens and threshold-sensitive cost | Do not extrapolate Sol's short-context price above 272K |
Pin configurations
Record model IDs gpt-6-sol and gemini-3.8-flash, API tiers, effort settings, available tools and prompt versions.
Run at least two routes
Test both cost-optimized and quality-optimized configurations, with the same acceptance criteria and human validation.
Include failure economics
Count repeat calls, errors, tool steps, review time and input modalities; use observed data in the SXF calculator.
Official sources and SXF connected references
OPENAI MODEL CARDGPT-6 Sol pricing, context, tools and modalities
GOOGLE MODEL CARDGemini 3.8 Flash limits and input types
OPENAI BILLING272K long-context multiplier and tier prices
GOOGLE BILLINGGemini 2026/2027 effective rates
SXF MODELGPT-6 Sol full model reference
SXF MODELGemini 3.8 Flash full model reference
SXF FAMILYChoose another Google Gemini model
SXF SPECIALIST COMPARISONGemini 3.8 vs 3.7 Flash: migration & costs
SXF CROSS-PROVIDERGPT-6 Sol vs Claude Opus 5.5
NEWER SOL MODELGPT-6.1 Sol: separate model ID and specifications
Editorial fact check: . Data and benchmarks can change; vendor and independent evidence are attributed separately. This is a decision framework, not a paid placement, API quote or claimed SXF-run benchmark.
OFFICIAL SOURCES
OpenAI · GPT-6 Sol model card↗OpenAI · GPT-6 model guidance↗OpenAI · API pricing↗OpenAI · API changelog↗Google AI · Gemini 3.8 Flash model page↗Google AI · What’s new in Gemini 3.8 Flash↗Google AI · Gemini API pricing↗Google AI · Models overview↗QUICK ANSWERS
Which is cheaper, GPT-6 Sol or Gemini 3.8 Flash?
At current Standard paid API rates through December 31, 2026, Gemini 3.8 Flash has lower headline text-token pricing: $0.75 input and $3.75 output per million tokens versus $2 and $10 for GPT-6 Sol. Google states that Gemini 3.8 Flash Standard pricing rises to $1.50 input and $7.50 output on January 1, 2027.
Which has the larger context window?
The nominal context windows are effectively the same size: GPT-6 Sol lists 1,050,000 tokens and Gemini 3.8 Flash lists 1,048,576 input tokens. GPT-6 Sol has the larger maximum output at 128,000 tokens versus 65,536 for Gemini 3.8 Flash.
Which model supports more input modalities?
Gemini 3.8 Flash supports text, image, video, audio and PDF input. GPT-6 Sol supports text and image input on its model card. Both produce text output.
How do reasoning controls differ?
GPT-6 Sol supports reasoning effort levels none, low, medium, high, xhigh and max, with medium as the default. Gemini 3.8 Flash supports low, medium and high thinking levels, also defaulting to medium.
Which is better for coding?
Both vendors explicitly position the models for software engineering. OpenAI describes GPT-6 Sol as built for complex coding and agentic workflows, while Google describes Gemini 3.8 Flash as engineered for long-horizon software engineering and complex multi-file refactoring. A defensible choice requires repository-specific evaluations rather than a universal coding winner.
Which is better for AI agents?
Both models expose serious agent tool stacks. GPT-6 Sol is designed around OpenAI's Responses API. Gemini 3.8 Flash supports function calling, code execution, file search, Search grounding, Maps grounding, URL context and computer use in preview. The better fit depends on the surrounding platform, tools, permissions and cost target.
How does long-context pricing differ?
OpenAI states that GPT-6 Sol prompts above 272K input tokens use 2x input and cache rates and 1.5x output rates for the full request. Google's current Gemini 3.8 Flash pricing page lists its token rates and caching charges without an equivalent 272K long-context multiplier.
Does Gemini 3.8 Flash pricing change in 2027?
Yes. Google's introductory Standard paid pricing of $0.75 input and $3.75 output per million tokens runs through December 31, 2026. Starting January 1, 2027, Google lists $1.50 input and $7.50 output.
Does either model support real-time voice?
The standard Gemini 3.8 Flash model does not support the Live API; Google provides separate Gemini 3.8 Live models. GPT-6 Sol's model page lists text and image as supported model modalities and does not list audio or video as model inputs.