Grok 4.20.0309 Reasoning vs Grok 4.3
Grok 4.20.0309 Reasoning (xAI, 2,000,000-token context) versus Grok 4.3 (xAI, 1,000,000-token context). Grok 4.3 is cheaper by 53% on a blended token mix. Grok 4.3 uniquely supports structured output (json schema) and prompt caching. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Grok 4.20.0309 Reasoning vs Grok 4.3
Grok 4.20.0309 Reasoning and Grok 4.3 target overlapping workloads but differ sharply on economics. Grok 4.3 runs roughly 53% cheaper on a blended input-plus-output token mix, which translates to approximately $4,350 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Grok 4.20.0309 Reasoning ships a 2,000,000-token context window, 2.0x larger than Grok 4.3's 1,000,000 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 1,000,000 tokens, the extra context on Grok 4.20.0309 Reasoning is insurance you may never use — and Grok 4.3 may win on other axes.
On capability surface area, the models diverge: Grok 4.3 supports structured output (json schema) where the other does not; Grok 4.3 supports prompt caching where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: grok-4-3
provider: xai
fallback:
model: grok-4-20-0309-reasoning
provider: xai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Grok 4.20.0309 Reasoning | Grok 4.3 | |
|---|---|---|
| Input price | $2.00/M | $1.25/M |
| Output price | $6.00/M | $2.50/M |
| Context window | 2,000,000 | 1,000,000 |
| Max output | 2,000,000 | 1,000,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | — | ✓ |
| Structured output | — | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Grok 4.20.0309 Reasoning | Grok 4.3 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $960 /mo | $525 /mo | $435/mo |
| Mid-market 100K requests/day | $9,600 /mo | $5,250 /mo | $4,350/mo |
| Enterprise 1M requests/day | $96,000 /mo | $52,500 /mo | $43,500/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
You're cost-sensitive at scale — Grok 4.3 runs ~53% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Grok 4.20.0309 Reasoning fits 2,000,000 tokens versus the other model's 1,000,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
You re-send the same large system prompt across requests — Grok 4.3 supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Grok 4.20.0309 Reasoning, switching to Grok 4.3 means re-architecting that path (and vice versa).
- • Structured output (JSON schema)
- • Prompt caching
Capabilities both share (4)
- ✓ Function calling
- ✓ Vision input
- ✓ Streaming
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 50% when moving from Grok 4.20.0309 Reasoning (2,000,000) to Grok 4.3 (1,000,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 2,000,000 on Grok 4.20.0309 Reasoning vs 1,000,000 on Grok 4.3. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Grok 4.3 has capabilities Grok 4.20.0309 Reasoning lacks: Structured output (JSON schema), Prompt caching. Worth wiring through the agent design before commit.
How to A/B test Grok 4.20.0309 Reasoning vs Grok 4.3 in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Grok 4.20.0309 Reasoning primary, mirror 20% of traffic to Grok 4.3 in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Grok 4.20.0309 Reasoning vs Grok 4.3
Which is cheaper, Grok 4.20.0309 Reasoning or Grok 4.3? ▾
Grok 4.3 is cheaper by roughly 53% on a blended input + output token mix. Input prices are $2.00/M for Grok 4.20.0309 Reasoning versus $1.25/M for Grok 4.3; output prices are $6.00/M versus $2.50/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Grok 4.20.0309 Reasoning versus Grok 4.3? ▾
Grok 4.20.0309 Reasoning supports up to 2,000,000 tokens of context. Grok 4.3 supports up to 1,000,000 tokens. Grok 4.20.0309 Reasoning has the larger window by a factor of 2.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Grok 4.20.0309 Reasoning and Grok 4.3 both support tool calling? ▾
Yes — both Grok 4.20.0309 Reasoning and Grok 4.3 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Which model supports prompt caching for cost reduction? ▾
Grok 4.3 supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Grok 4.3 gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Grok 4.20.0309 Reasoning over Grok 4.3? ▾
Your workload needs long context — Grok 4.20.0309 Reasoning fits 2,000,000 tokens versus the other model's 1,000,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
When should I choose Grok 4.3 over Grok 4.20.0309 Reasoning? ▾
You're cost-sensitive at scale — Grok 4.3 runs ~53% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. You re-send the same large system prompt across requests — Grok 4.3 supports prompt caching, cutting input cost on repeat hits.
How do I A/B test Grok 4.20.0309 Reasoning against Grok 4.3 in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.