Grok 4 is xAI's cheapest model at $2/1M input tokens — 0% less than the flagship Grok 4. For the best capability per dollar, Grok 4.5 is the smarter budget pick.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
Grok 4.5 is the safest overall answer here when you want the strongest default instead of the lowest list price.
Mistral: Mistral Nemo is the lower-cost option to start with when you still need useful output at scale.
Grok 4 is the better pick when response speed matters more than maximum reasoning depth.
Grok 4 is the lowest-cost xAI model: $2/1M input, $6/1M output.
Grok 4.5 is the best capability-per-dollar pick (budget score 62/100).
Grok 4 costs 1x more on input — reserve it for work where quality is the bottleneck.
Choose Grok 4 for high-volume, low-stakes tasks like classification, extraction, and drafts.
Choose Grok 4.5 as the everyday default if you want one budget model.
Route only the hardest tasks to Grok 4 — a two-tier setup usually cuts spend 60–80%.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
xAI / Balanced / Aug 27, 2026
Finishes agent tasks in half the turns — cheap where it counts.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need a published SWE-bench score to justify the pick, or your prompts routinely cross 200K tokens where the price doubles.
The fastest way to see where the recommendation shifts when your priority changes.
Strong coding value with 2M context — an underrated pick at this price.
Best cost-per-solved-task coding agent — efficiency over ceiling.
Finishes agent tasks in half the turns — cheap where it counts.
Every figure below is the provider's list price or a published capability score — the same numbers the recommendation on this page is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Grok 4.5xAI | $2.00/1M | $6.00/1M | $32 | 500k tokens | Fast | 94 | 72 | 84 |
| Grok 4xAI | $2.00/1M | $6.00/1M | $32 | 2M tokens | Fast | 92 | 70 | 86 |
| Grok 4.6xAI | $2.00/1M | $6.00/1M | $32 | 500k tokens | Fast | 86 | 84 | 88 |
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
xAI's first coding- and agent-focused model — the first full-scale deployment of the 1.5T-parameter V9 MoE base, trained with real developer-session data from Cursor.
You need the highest solve rate per attempt or verified low hallucination — Claude Opus 5 is the safer premium pick.
The efficiency play among coding agents. Grok 4.5 wins on cost-per-solved-task and marathon sessions, not raw capability. If your agent bill is the problem, it's the answer; if quality ceiling is the problem, it isn't.
Released July 8, 2026 on the 1.5T-parameter V9 base. Pricing verified on docs.x.ai: $2/$6 under 200K prompt tokens, $4/$12 above. Full access initially gated to SuperGrok Heavy; staged rollout to SuperGrok $30 tier. EU availability lagged launch.
xAI's latest flagship with strong coding benchmark performance, a 2M token context window, and aggressive pricing at $2/$6 per million tokens.
You need the highest writing quality or the most reliable production-grade output — Claude wins both.
Strong coding benchmark with excellent value. The $2/$6 pricing and 2M context make it competitive with more expensive alternatives.
Best when you want near-flagship coding quality with a massive context window at a mid-tier price.
xAI's long-horizon agent model — it finishes agentic tasks in roughly half the turns of its rivals, which makes it cheaper in practice than its per-token price suggests.
You need a published SWE-bench score to justify the pick, or your prompts routinely cross 200K tokens where the price doubles.
Buy it for turn efficiency, not for benchmark ceilings. On long agent runs, finishing in half the turns beats a model that scores two points higher and takes twice as many round trips.
Released August 12, 2026, succeeding Grok 4.5. Long-context billing is a cliff, not a ramp: at 200K tokens and above the whole request is charged at $4/$12. DeepSWE 65.9%, CursorBench 3.2 70.8%, FrontierCode 1.1 Extended 61.3%, Terminal-Bench 3.0 26.5%, APEX-Agents 57.5%.
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Grok 4 at $2/1M input and $6/1M output tokens. Strong coding value with 2M context — an underrated pick at this price.
Grok 4.5 is the best capability-per-dollar pick in xAI's lineup (budget score 62/100). It handles fast, token-efficient coding agents well — step up to Grok 4 only where quality visibly falls short.
Grok 4 costs $2/1M input vs $2/1M for Grok 4 — a 0% saving on input tokens.
Grok 4 — 2M tokens at $2/1M input. Context is where budget models are least compromised: you usually lose reasoning depth before you lose window size, so a cheap model is often a perfectly good choice for summarising or extracting from long documents.
Claude Opus 4.6 and Sonnet 4.6 lead on pure coding benchmarks. Less established ecosystem and tooling than OpenAI or Anthropic. Avoid it if you need the highest writing quality or the most reliable production-grade output — Claude wins both.
On a moderate workload of 10M input and 2M output tokens, Grok 4 runs about $32.00 against $32.00 for Grok 4 — a difference of $0.00 a month at the same volume. Output tokens dominate the bill on both, so the length of the responses you generate matters far more than the length of your prompts.
Mixing is almost always cheaper for the same quality. Route high-volume, low-stakes work — classification, extraction, first drafts, routine agent steps — to Grok 4, and reserve Grok 4 for the calls where a wrong answer costs real time. Teams that split this way typically cut spend substantially without a quality drop anyone notices, because most tokens in a real workload are not hard problems.