Grok 4.3 vs Gemini 3.5 Flash: Which AI Powers Your Agents Better in 2026?

June 24, 2026 Finixio Digital

Featured Snippet Answer

Grok 4.3 is the better raw-cost choice for output-heavy reasoning agents, while Gemini 3.5 Flash is the stronger default for multimodal, coding, and Google-grounded workflows. Both support 1M-token context windows, but their economics differ sharply: Grok 4.3 is officially priced at $1.25/M input and $2.50/M output, while Gemini 3.5 Flash is $1.50/M input and $9.00/M output. Through CometAPI, both are available at about 20% below official pricing.

In the fast-evolving AI landscape of mid-2026, Grok 4.3 (xAI) and Gemini 3.5 Flash (Google DeepMind) represent two powerful approaches: Grok emphasizes speed, agentic efficiency, and aggressive pricing, while Gemini 3.5 Flash delivers near-frontier intelligence with strong multimodal and coding capabilities at Flash-tier speeds.

Whether you’re building autonomous agents, scaling RAG pipelines, or optimizing coding workflows, this guide provides data-backed insights to help you choose — and save money via CometAPI.

What is Grok 4.3?

Grok 4.3, released by xAI around April 30, 2026, is a flagship reasoning model designed for agentic workflows, instruction-following, high factual accuracy, and complex multi-step tasks. For developers, Grok 4.3 is especially attractive when the workload is text-heavy and output-heavy: research synthesis, multi-step planning, knowledge work, document Q&A, support automation, and agents that may need many repair loops. Kilo Code’s coding benchmark page lists Grok 4.3 with a 42.2 AA Coding Index, 47.3% on SciCode, 37.9% on TerminalBench Hard, 64.3% on long-context reasoning, and 81.3% on IFBench instruction following.

Key Features:

Context Window: 1 million tokens (with no strict output limit in many setups), ideal for long-document analysis, deep research, and persistent agent memory.
Reasoning: Configurable effort levels (none/low/medium/high; default low) for balancing speed and depth.
Multimodal: Text and image inputs; strong tool calling, structured outputs, and native support for agentic environments (code execution, web/X search, files).
Strengths: Excels in agentic tasks (e.g., high Elo on GDPval-AA benchmarks), low hallucination rates in some evaluations, and real-world reliability for instruction following (e.g., ~81% IFBench, strong τ²-Bench).
API Pricing (xAI): $1.25 / $2.50 per 1M input/output tokens. Prompt caching and optimizations available.

Grok 4.3 builds on prior versions with improved architecture, better agentic performance, and competitive intelligence scores (e.g., ~38-53 on Artificial Analysis Intelligence Index depending on configuration).

What is Gemini 3.5 Flash?

Gemini 3.5 Flash is Google’s newest Flash-tier model built for high-speed, agentic, multimodal, and coding workflows. Gemini 3.5 Flash is generally available, stable, and ready for scaled production use, with sustained frontier performance in coding, agentic execution, and long-horizon tasks. It supports a 1M-token input context window, up to 65K output tokens, thinking levels, and the same broad Gemini 3 family tool set, except Computer Use is not currently supported.

Key Features:

Context Window: 1 million tokens input, up to ~65K output tokens.
Multimodal: Strong native support for text, images, audio, video—giving it an edge in multimedia workflows.
Reasoning & Tools: Built-in thinking modes, native tool use, function calling, and excellent performance on coding/agent benchmarks.
Strengths: Leads or competes on intelligence vs. speed Pareto frontier, strong multimodal (e.g., high MMMU-Pro), reduced hallucinations, and fast execution for production agents.
API Pricing (Google): Approximately $1.50 / $9.00 per 1M input/output tokens (varies by provider/endpoint; caching discounts available).

Gemini 3.5 Flash often punches above its “Flash” tier, rivaling larger models on many metrics while maintaining low latency.

Grok 4.3 vs Gemini 3.5 Flash Comparison Table

Category	Grok 4.3	Gemini 3.5 Flash	Practical Takeaway
Provider	xAI	Google DeepMind	Both are major proprietary models
Release window	April 2026	May 2026	Gemini is newer by public release timing
Context window	1M tokens	1M input tokens, up to 65K output	Headline context is effectively tied
Input modalities	Text, image	Text, image, audio/speech, video	Gemini is broader for multimodal agents
Output	Text	Text	Tie for text-generation use cases
Official input price	$1.25/M	$1.50/M	Grok is cheaper
Official output price	$2.50/M	$9.00/M	Grok is much cheaper for verbose agents
CometAPI price	$1/M input, $2/M output	$1.2/M input, $7.2/M output	CometAPI lists about 20% savings for both
Reasoning control	none/low/medium/high	minimal/low/medium/high, medium default	Both expose useful effort controls
Artificial Analysis Intelligence Index	53	55	Gemini slightly leads on this index
GDPval-AA	1500 Elo	1656 Elo	Gemini leads on reported real-world work tasks
Coding	42.2 AA Coding Index, 37.9 TerminalBench Hard	76.2 Terminal-bench 2.1, 55.1 SWE-Bench Pro	Gemini has stronger disclosed coding-agent results
Tool use	Function calling, structured outputs, server-side tools	Search, Maps grounding, File Search, URL Context, Code Execution, function calling	Gemini has broader built-in tool ecosystem
Best fit	Cost-efficient reasoning and output-heavy agents	Multimodal, coding, tool-rich agents	Use routing instead of a single-model default

Pricing Comparison: Grok 4.3 vs Gemini 3.5 Flash

Official API Pricing

Grok 4.3 is cheaper on both input and output. xAI lists grok-4.3 at $1.25/M input, $0.20/M cached input, and $2.50/M output. It also lists server-side tool costs: Web Search, X Search, and Code Execution at $5 per 1,000 calls; File Attachments at $10 per 1,000 calls; and Collections Search at $2.50 per 1,000 calls.

Gemini 3.5 Flash Standard is officially $1.50/M input and $9.00/M output. Batch and Flex pricing are lower, at $0.75/M input and $4.50/M output, which matters if your workload can tolerate asynchronous or lower-priority processing. Google Search grounding is listed with 5,000 prompts per month included across Gemini 3, then $14 per 1,000 search queries.

The biggest pricing difference is output. Gemini 3.5 Flash output is 3.6x Grok 4.3’s official output price. That matters because agents do not only answer once. They plan, call tools, inspect results, repair mistakes, and produce intermediate reasoning or verbose final reports. Even when input pricing looks close, output pricing can dominate real bills.

CometAPI Recommendation: CometAPI aggregates 500+ models (including both Grok 4.3 and Gemini 3.5 Flash) with competitive rates, often ~20% savings, unified billing, failover routing, and no vendor lock-in. Access both via one API key for seamless switching.

On CometAPI, expect attractive pricing like Gemini 3.5 Flash around $1.2/M (example) and strong Grok support. Test free credits and monitor usage in one dashboard — ideal for agents that benefit from routing logic.

What a Typical Agent Run Actually Costs

Assume a medium-complexity agent task: 50K input tokens (prompt + context + tools) + 5K output tokens, with some tool calls.

Grok 4.3 (direct): ~$0.0625 input + $0.0125 output = ~$0.075 per run. With caching/repeated context: even lower (~$0.02–0.05).
Gemini 3.5 Flash (direct): ~$0.075 input + $0.045 output = ~$0.12 per run.
Scaled Example (1,000 runs/month): Grok ~$75; Gemini ~$120. CometAPI can reduce this further with optimization and volume.

For high-volume agents (e.g., autonomous coding or research), Grok 4.3 often wins on pure cost; Gemini shines when multimodal or deeper reasoning reduces retry costs. Use CometAPI’s routing to dynamically select based on task (e.g., cheap Grok for simple steps, Gemini for complex coding).

Benchmark Performance

Core Reasoning and Knowledge

Artificial Analysis gives Gemini 3.5 Flash a small edge on its Intelligence Index: 55 versus Grok 4.3’s 53. That is not a huge gap, but it is directionally meaningful. Gemini also leads in GDPval-AA, with Google DeepMind reporting 1656 Elo versus Artificial Analysis reporting 1500 Elo for Grok 4.3.

Grok’s strength is cost-per-intelligence. Artificial Analysis notes that Grok 4.3 sits on the intelligence-versus-cost Pareto frontier and cost about $395 to run the Intelligence Index evaluations. Gemini 3.5 Flash scored higher, but Artificial Analysis reports it cost about $1,551.60 to run the Intelligence Index. That does not mean Gemini is “bad value.” It means Gemini may use more tokens and has higher output pricing, so the total cost of agentic evaluations can rise quickly.

Coding

Gemini 3.5 Flash has the cleaner public story for coding agents. Google DeepMind reports 76.2% on Terminal-bench 2.1 and 55.1% on SWE-Bench Pro Public. It also beats Gemini 3 Flash and Gemini 3.1 Pro on several of Google’s listed agentic/coding benchmarks, including MCP Atlas and Terminal-bench 2.1.

Grok 4.3 can still be useful for coding, especially for explanation, refactoring plans, test generation, and cost-sensitive code review. But its disclosed coding-agent numbers are less dominant. Kilo Code reports 42.2 on the AA Coding Index, 47.3% on SciCode, and 37.9% on TerminalBench Hard. For serious autonomous software-engineering agents, Gemini 3.5 Flash is the safer default to test first.

Tool Use & Agentic

Gemini 3.5 Flash is built deeply into Google’s tool ecosystem. Google lists Search, Maps grounding, File Search, Code Execution, URL Context, function calling, combined tool use, structured outputs with tools, multimodal function responses, and thought signatures. It does not currently support Computer Use, which Google explicitly notes.

Grok 4.3 supports function calling and structured outputs, and xAI’s platform includes Web Search, X Search, Code Execution, file attachments, collections search, and remote MCP tools. The key difference is that xAI separately prices several built-in server-side tool invocations. That is not a problem, but it means cost monitoring matters more in autonomous workflows.

Latency and Speed

Gemini 3.5 Flash often wins on raw speed and throughput (higher tok/s in many reports). Grok 4.3 is competitive, especially for its intelligence level, with low TTFT in optimized setups.

For real-time apps, Gemini; for deep reasoning agents, Grok’s balance wins on CometAPI with load balancing.

Context Window: Does 200K vs 128K Matter? (Both at 1M)

Both support 1M tokens—plenty for entire codebases, books, or long histories. The “200K vs 128K” refers to older comparisons; current gen makes it largely irrelevant for most. Long-context reasoning: Grok strong in LCR; Gemini in needle-in-haystack multimodal.

CometAPI Tip: Our context compression and caching make 1M feel even larger and cheaper.

How CometAPI Handles Model Selection in Agent Workflows

The practical CometAPI recommendation is to treat model choice as a routing problem.

First, classify each request. Is it a coding task, a multimodal task, a long-document synthesis task, a customer-support answer, a grounded research task, or a cheap classification step?

Second, route by model economics. Grok 4.3 should be tested first for output-heavy reasoning, long reports, summarization, planning, and high-volume agent loops. Gemini 3.5 Flash should be tested first for coding agents, multimodal document/media ingestion, Google-grounded workflows, and complex tool orchestration.

Third, set budget controls. Cap max output tokens, choose lower reasoning effort for simple steps, log input/output/tool tokens separately, and measure cost per successful completed task rather than cost per API call.

Fourth, keep fallbacks. CometAPI’s pricing emphasizes unified billing, built-in failover routing, and single-entry cost visibility versus managing each provider directly. That matters because model performance and availability can shift. In production, your app should not depend on one model always being best.

Final Recommendation

Choose Grok 4.3 if your main concern is cost-efficient reasoning at scale. Its low output price makes it compelling for agents that produce long responses, run many loops, or summarize large knowledge bases.

Choose Gemini 3.5 Flash if your main concern is multimodal capability, coding-agent performance, and Google-native tool use. Its output is more expensive, but the benchmark profile and tool ecosystem can justify the price for higher-value workflows.

Choose CometAPI if you want to compare both without rebuilding your stack. Start with a two-model router: Gemini 3.5 Flash for multimodal/coding/tool-rich tasks, Grok 4.3 for cost-sensitive reasoning and long-form generation, then refine routing with your own task-level benchmarks.

Ready to implement? Start with CometAPI today for unified access and savings.

FAQs

Is Grok 4.3 better than Gemini 3.5 Flash?

Not universally. Grok 4.3 is usually better on raw cost, especially output-heavy workloads. Gemini 3.5 Flash has stronger disclosed multimodal, coding, and tool-use benchmark coverage.

Which model is cheaper?

Grok 4.3 is cheaper. Officially, Grok 4.3 is $1.25/M input and $2.50/M output, while Gemini 3.5 Flash Standard is $1.50/M input and $9.00/M output. CometAPI lists Grok at $1/M and $2/M, and Gemini at $1.2/M and $7.2/M.

Which model is better for AI agents?

Gemini 3.5 Flash is better for multimodal and tool-rich agents. Grok 4.3 is better for cost-sensitive reasoning agents that generate lots of text.

Which model is better for coding?

Gemini 3.5 Flash has stronger published coding-agent benchmark results, including 76.2% on Terminal-bench 2.1 and 55.1% on SWE-Bench Pro Public.

Do both models support 1M context?

Yes. Current xAI and Google docs list 1M-token context for Grok 4.3 and Gemini 3.5 Flash. The practical limit is often cost, latency, and relevance rather than the headline window.

Should I use CometAPI instead of direct provider APIs?

For teams comparing multiple models, CometAPI can simplify integration, billing, pricing visibility, and failover. Direct APIs may still be preferable if you need a provider-specific feature that is not exposed through an aggregator.

What is the best production setup?

Use a router. Send coding, multimodal, and Google-grounded tasks to Gemini 3.5 Flash; send output-heavy reasoning and summarization to Grok 4.3; track cost per successful task; and keep fallback models available through CometAPI.