GPT-5.6 Sol vs Grok 4.5 vs Gemini 3.6 Flash: Build a Smarter
GPT-5.6 Sol vs Grok 4.5 vs Gemini 3.6 Flash: stop asking which model is best and build an agent router that sends each request to the model that fits it.
This article is published in English.
Sources: GPT-5.6 Sol vs Grok 4.5 vs Gemini 3.6 Flash: Build a Smarter Agent Router
The following notes reconstruct a practical path around “GPT-5.6 Sol vs Grok 4.5 vs Gemini 3.6 Flash: Build a Smarter Agent Router”. Emphasis stays on contracts, checks, and drop-in code placeholders rather than motivational framing.
Stop asking which model is best. Route each agent task by risk, context, tools, output size, and total cost.
When working through the Stop asking which model stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
User request → One powerful model → Tools → Final answer
Task
↓
Classification and risk analysis
↓
Model selection
↓
Execution
↓
Validation
↓
Escalation when required
The models being routed
When working through the The models being routed stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
GPT-5.6 Sol
When working through the GPT-5 6 Sol stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
GPT-5.6 Sol long-context pricing
When working through the GPT-5 6 Sol long-context stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
Input: $10 per million tokens
Output: $45 per million tokens
Grok 4.5 long-context pricing
When working through the Grok 4 5 long-context stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs. When working through the Grok 4 5 long-context stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
Input: $4 per million tokens
Cached input: $0.60 per million tokens
Output: $12 per million tokens
Input: 100,000 tokens
Output: 10,000 tokens
Input: 50,000 tokens
Output: 50,000 tokens
Input tokens = 3 × output tokens
Total model and tool cost
─────────────────────────
Accepted completed tasks
What the router should evaluate
The What the router should stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos.
1. Task type
The 1 Task type stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos.
classification
extraction
repository_search
planning
implementation
debugging
architecture
migration
security_review
final_review
computer_use
2. Risk
The 2 Risk stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos. The 2 Risk stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
Low:
Documentation, formatting, metadata, test generation
Medium:
Contained feature work, refactoring, ordinary bug fixes
High:
Authentication, payments, permissions, infrastructure,
database migrations, security-sensitive code
3. Context size
For the 3 Context size stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine.
4. Expected output size
For the 4 Expected output size stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine.
5. Required tools
For the 5 Required tools stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine. For the 5 Required tools stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
6. Previous failures
When working through the 6 Previous failures stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
A practical routing policy
When working through the A practical routing policy stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
Route to Gemini 3.6 Flash for:
When working through the Route to Gemini 3 stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs. When working through the Route to Gemini 3 stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
Route to Grok 4.5 for:
The Route to Grok 4 stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos.
Route to GPT-5.6 Sol for:
The Route to GPT-5 6 stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos.
TypeScript reference implementation
The TypeScript reference implementation stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos. The TypeScript reference implementation stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
type ModelId =
| "gpt-5.6-sol"
| "grok-4.5"
| "gemini-3.6-flash";
type TaskKind =
| "classification"
| "extraction"
| "repository_search"
| "planning"
| "implementation"
| "debugging"
| "architecture"
| "migration"
| "security_review"
| "final_review"
| "computer_use";
type RiskLevel = "low" | "medium" | "high";
interface AgentTask {
kind: TaskKind;
risk: RiskLevel;
estimatedInputTokens: number;
estimatedOutputTokens: number;
previousFailures: number;
requiresComputerUse?: boolean;
requiresXSearch?: boolean;
requiresMcp?: boolean;
}
interface ModelPricing {
inputPerMillion: number;
outputPerMillion: number;
cachedInputPerMillion?: number;
contextWindow: number;
longContext?: {
threshold: number;
inputPerMillion: number;
outputPerMillion: number;
cachedInputPerMillion?: number;
};
}
const MODEL_PRICING: Record<ModelId, ModelPricing> = {
"gpt-5.6-sol": {
inputPerMillion: 5,
outputPerMillion: 30,
cachedInputPerMillion: 0.5,
contextWindow: 1_050_000,
longContext: {
threshold: 272_001,
inputPerMillion: 10,
outputPerMillion: 45,
cachedInputPerMillion: 1,
},
},
"grok-4.5": {
inputPerMillion: 2,
outputPerMillion: 6,
cachedInputPerMillion: 0.3,
contextWindow: 500_000,
longContext: {
threshold: 200_000,
inputPerMillion: 4,
outputPerMillion: 12,
cachedInputPerMillion: 0.6,
},
},
"gemini-3.6-flash": {
inputPerMillion: 1.5,
outputPerMillion: 7.5,
contextWindow: 1_000_000,
},
};
function estimateCost(
model: ModelId,
inputTokens: number,
outputTokens: number,
): number {
const pricing = MODEL_PRICING[model];
if (inputTokens + outputTokens > pricing.contextWindow) {
throw new Error(
`${model} context window exceeded: ` +
`${inputTokens + outputTokens} tokens`,
);
}
const activePricing =
pricing.longContext &&
inputTokens >= pricing.longContext.threshold
? pricing.longContext
: pricing;
return (
(inputTokens / 1_000_000) *
activePricing.inputPerMillion +
(outputTokens / 1_000_000) *
activePricing.outputPerMillion
);
}
function chooseModel(task: AgentTask): ModelId {
// Hard tool requirements come first.
if (task.requiresXSearch) {
return "grok-4.5";
}
if (task.requiresMcp) {
return "gpt-5.6-sol";
}
// Escalate tasks that repeatedly fail.
if (task.previousFailures >= 2) {
return "gpt-5.6-sol";
}
// High-risk work receives stronger reasoning or review.
if (
task.risk === "high" ||
task.kind === "architecture" ||
task.kind === "migration" ||
task.kind === "security_review" ||
task.kind === "final_review"
) {
return "gpt-5.6-sol";
}
// Gemini is suitable for routine UI automation.
if (
task.requiresComputerUse ||
task.kind === "computer_use"
) {
return "gemini-3.6-flash";
}
// Large prompts can make Grok's long-context tier
// less attractive.
if (task.estimatedInputTokens >= 200_000) {
return "gemini-3.6-flash";
}
// Output-heavy coding tasks can favour Grok.
const inputToOutputRatio =
task.estimatedInputTokens /
Math.max(task.estimatedOutputTokens, 1);
if (
task.kind === "implementation" ||
task.kind === "debugging"
) {
return inputToOutputRatio < 3
? "grok-4.5"
: "gemini-3.6-flash";
}
// Cheap default for low-risk, input-heavy work.
return "gemini-3.6-flash";
}
function routeTask(task: AgentTask) {
const model = chooseModel(task);
const estimatedCost = estimateCost(
model,
task.estimatedInputTokens,
task.estimatedOutputTokens,
);
return {
model,
estimatedCost,
reason: buildRoutingReason(task, model),
};
}
function buildRoutingReason(
task: AgentTask,
model: ModelId,
): string {
if (task.requiresXSearch) {
return "Task requires native X search.";
}
if (task.previousFailures >= 2) {
return "Escalated after repeated failures.";
}
if (task.risk === "high") {
return "High-risk task requires stronger reasoning.";
}
if (task.estimatedInputTokens >= 200_000) {
return "Large input makes long-context pricing relevant.";
}
if (model === "grok-4.5") {
return "Output-heavy coding task.";
}
return "Low-risk, input-heavy agent task.";
}
Add validation before escalation
For the Add validation before escalation stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine.
interface RunResult {
model: ModelId;
success: boolean;
inputTokens: number;
outputTokens: number;
toolCalls: number;
testsPassed?: boolean;
lintPassed?: boolean;
typecheckPassed?: boolean;
changedFiles: string[];
estimatedCost: number;
latencyMs: number;
failureReason?: string;
}
function shouldEscalate(result: RunResult): boolean {
if (!result.success) return true;
if (result.testsPassed === false) return true;
if (result.lintPassed === false) return true;
if (result.typecheckPassed === false) return true;
return false;
}
Use an escalation ladder
For the Use an escalation ladder stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine.
Low-risk agent task
Gemini 3.6 Flash
↓ failure
Grok 4.5
↓ failure
GPT-5.6 Sol
Grok 4.5
↓ failed tests or repeated loop
GPT-5.6 Sol
GPT-5.6 Sol implementation
↓
Independent review by another model
↓
Automated checks
↓
Human approval
Separate planning, execution, and review
For the Separate planning execution and stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine. For the Separate planning execution and stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
Cost-conscious coding workflow
When working through the Cost-conscious coding workflow stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
Gemini 3.6 Flash
→ Gather repository context
→ Classify the task
→ Identify relevant files
Grok 4.5
→ Implement the change
→ Run tests
→ Repair ordinary failures
GPT-5.6 Sol
→ Review only if risk is high
→ Resolve ambiguous failures
High-risk migration workflow
When working through the High-risk migration workflow stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs.
GPT-5.6 Sol
→ Design the migration plan
→ Identify rollback requirements
Grok 4.5
→ Implement isolated migration steps
GPT-5.6 Sol
→ Review the patch and failure modes
Automated pipeline
→ Test, lint, scan and stage
Human reviewer
→ Approve production execution
Build an evaluation dataset from real work
When working through the Build an evaluation dataset stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Log request id, model id, and latency on every call. Without that trail, intermittent provider errors look like application bugs. When working through the Build an evaluation dataset stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
Task category
Selected model
Input and output tokens
Tool calls
Latency
Retries
Estimated cost
Test result
Human corrections
Files reverted
Final acceptance status
Escalation path
Cost per accepted task
The Cost per accepted task stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos.
Total cost, including retries
─────────────────────────────
Number of accepted tasks
First-attempt success rate
The First-attempt success rate stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos.
Tasks accepted without retry
────────────────────────────
Total tasks
Human correction time
The Human correction time stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments. Pin the interpreter and dependency lockfile before teaching the loop. Drift between laptop and CI is the most common silent break for API demos. The Human correction time stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish.
Unnecessary change rate
For the Unnecessary change rate stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine.
The router should remain replaceable
For the The router should remain stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Separate client construction from the message loop so providers can be swapped without rewriting the conversation state machine.
Gemini is always cheap.
Grok is always best for implementation.
Sol is always the strongest.