Claude Fable 5 is the strongest alternative to Grok 4.6 — it scores 100 vs 88 on research at $10/1M input (Grok 4.6 costs $2/1M). Gemini 3.6 Flash is the budget swap: $1.5/1M input is 25% cheaper. Kimi K3 is the top open-weight option if you want a model you can self-host.
Last verified Aug 27, 2026/Model data modified Aug 27, 2026
Rankings refresh dailyScored on 6 criteriaNo paid rankings
AnthropicPremium
Input cost
$10.00/1M
Context
1M tokens
Speed
Deliberate
Clear recommendation block
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
Grok 4.6 is the better pick when response speed matters more than maximum reasoning depth.
xAIBalanced
Best for
Long-running agents and multi-step codebase work
Price
$2.00/1M
Context
500k tokens
Why this page recommends it
Claude Fable 5 beats Grok 4.6 on research (100 vs 88) at $10/1M input tokens.
Gemini 3.6 Flash cuts input cost by 25% ($1.5 vs $2/1M) while scoring 91/100 on research.
Kimi K3 is open-weight — self-host it or run it via low-cost API providers at $3/1M input.
Decision notes
Choose Claude Fable 5 when you want the closest overall replacement — it targets the hardest coding tasks, autonomous multi-step agents, and frontier-grade reasoning.
Choose Gemini 3.6 Flash when token volume matters more than peak quality — it is 25% cheaper on input.
Staying with xAI? Grok 4 is the strongest in-house switch at $2/1M input.
Interactive decision lab
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Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
#1Claude Fable 591 pts
#2Claude Mythos 590 pts
#3Gemini 3.6 Flash88 pts
#4Kimi K388 pts
#5Grok 4.682 pts
Quality first
Claude Fable 5
Anthropic / Premium / Jun 9, 2026
91
New global #1 — 80.3% SWE-Bench Pro, the most capable model generally available.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
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.
The case for each model
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.
Anthropic's new Mythos-class flagship and the most capable coding model anyone can use — 80.3% SWE-Bench Pro, an 11-point jump over Opus 4.8. 1M context, 128K output, native parallel subagents. Released June 9, 2026.
Input
$10.00/1M
Output
$50.00/1M
Context
1M tokens
Speed
Deliberate
What people actually use it for
Autonomous agents that plan, write, run, and debug across an entire codebase with minimal supervision
Whole-repo refactors and PR review where accuracy outranks latency or cost
80.3% SWE-Bench Pro — the new #1, up from Opus 4.8's 69.2% and GPT-5.5's 58.6%
1932 on GDPval-AA, ahead of Opus 4.8 (1890) and GPT-5.5 (1769)
1M-token context at standard pricing, 128K max output per request
Mythos-class capability released for general use with new cyber-risk safeguards
Where it falls down
Priced at $10/$50 per 1M tokens — double Opus 4.8 ($5/$25)
Deliberate pace; not for latency-sensitive interactive apps
Standard-use safeguards block some high-risk security workloads (use Mythos 5 with partner access)
Skip it if
You are latency- or cost-sensitive, or your tasks don't need frontier-level reasoning — Opus 4.8 at half the price is plenty.
Our verdict
The strongest coding and reasoning model you can actually use today. 80.3% SWE-Bench Pro is an 11-point leap over Opus 4.8 — the biggest single-release jump of 2026. It costs 2× Opus 4.8, so use it for the hardest agentic and engineering work and keep Opus 4.8 or Sonnet for everyday volume.
Launched June 9, 2026 as the public, Mythos-class release. Available on the Claude API, Microsoft Foundry, and Google Vertex AI. Free for all users until June 22, 2026. Same underlying model as Claude Mythos 5, with safeguards that block specific high-risk cyber responses.
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.
Input
$2.00/1M
Output
$6.00/1M
Context
500k tokens
Speed
Fast
What people actually use it for
Long-horizon research agents that work a topic across many sequential steps
Working through an unfamiliar codebase over an extended agent session
Interactive and visual work where turn count drives the real bill
Where it wins
Roughly half the turns of competing models on agent tasks — real cost per completed task lands well below the sticker price
88.4% on Terminal-Bench 2.1 and 87.0% on VulcanBench v3
Artificial Analysis Intelligence Index of 61, 4th overall and within reach of Claude Opus 5
Where it falls down
No published SWE-bench Verified or SWE-bench Pro figure, so it cannot be compared directly on the standard coding leaderboard
GPT-5.6 Sol Max beats it on DeepSWE v1.1 and Terminal-Bench 3.0
Prompts of 200K tokens or more are billed at $4/$12 across the entire request, not just the overage
Skip it if
You need a published SWE-bench score to justify the pick, or your prompts routinely cross 200K tokens where the price doubles.
Our verdict
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%.
Google's efficiency-focused successor to 3.5 Flash — higher scores on every benchmark Google tested, ~17% fewer output tokens, and cheaper output pricing.
Input
$1.50/1M
Output
$7.50/1M
Context
1.0M tokens
Speed
Fast
What people actually use it for
Long-horizon engineering agents — DeepSWE 49% with up to 65% token reduction on long tasks
High-throughput multimodal work with video, audio, and PDF ingestion
Where it wins
Beats 3.5 Flash across the board: DeepSWE 49% vs 37%, MLE Bench 63.9% vs 49.7%, OSWorld-Verified 83.0% vs 78.4%
~17% fewer output tokens plus $7.50/1M output — compounds into materially cheaper agent runs
~280–304 tokens/sec with computer use built in as a native tool
Where it falls down
Point release, not a generational leap — Gemini 4 is teased but unreleased
Google's own lineup tops out at Flash tier for this generation; no 3.5/3.6 Pro exists
Skip it if
You need raw frontier reasoning ceiling — Claude Opus 5 and GPT-5.6 Sol lead the hardest tasks.
Our verdict
The best Google model for agents right now. Cheaper, faster, and stronger than 3.5 Flash with the best OSWorld computer-use score in its class. The default Gemini pick until Gemini 4 lands.
Released July 21, 2026 alongside 3.5 Flash-Lite and the gated 3.5 Flash Cyber. Knowledge cutoff March 2026. Batch $0.75/$3.75; cached input $0.15/1M.
Moonshot's 2.8-trillion-parameter multimodal reasoning flagship with always-on thinking — the largest open-weight model ever released and the closest Chinese challenger to the Western frontier.
Input
$3.00/1M
Output
$15.00/1M
Context
1M tokens
Speed
Deliberate
What people actually use it for
Hardest reasoning tasks — #4 of all models on AA Intelligence Index v4.1 (57.1), ahead of Claude Opus 4.8
Agentic coding at 81.2 FrontierSWE and 88.3 Terminal-Bench 2.0 (Moonshot-reported)
1M-context research synthesis with always-on extended thinking
Where it wins
AA Intelligence Index v4.1: 57.1 — #4 overall, behind only Claude Fable 5 and GPT-5.6 Sol, ahead of Claude Opus 4.8
Open weights (July 26, 2026) — at 2.8T parameters, the largest open-weight release in history
Where it falls down
Most expensive Chinese-lab model ever ($3/$15) with always-on thinking driving high output-token burn and slow responses
2.8T size makes self-hosting impractical despite open weights; consumer signups were paused July 19 over GPU capacity
Skip it if
You need fast responses or predictable output costs — always-on thinking burns tokens.
Our verdict
The first Chinese model to genuinely crowd the Western frontier — #4 on aggregate intelligence ahead of Opus 4.8. The always-on thinking makes it slow and output-heavy, so cost per task runs above the sticker price. A serious Opus-class alternative if latency isn't critical.
Released July 16, 2026; open weights July 26. Cache-hit input $0.30/1M. Subscriptions: Adagio (free) to Vivace $199/mo; full 1M context only on Allegro ($99) and up. New signups paused July 19 near GPU capacity, reopening in batches.
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FAQ
What is the best alternative to Grok 4.6?
Claude Fable 5 is the strongest overall alternative. It scores 100/100 on research (Grok 4.6: 88/100) and costs $10/1M input vs $2/1M. New global #1 — 80.3% SWE-Bench Pro, the most capable model generally available.
What is the cheapest good alternative to Grok 4.6?
Gemini 3.6 Flash at $1.5/1M input — 25% cheaper than Grok 4.6's $2/1M. It scores 91/100 on research, so expect a quality step down on the hardest tasks.
Is there an open-source alternative to Grok 4.6?
Yes — Kimi K3 is the strongest open-weight replacement for Grok 4.6, scoring 93/100 on research against Grok 4.6's 88/100. You can self-host it or run it through hosted APIs at $3/1M input (Grok 4.6 costs $2/1M), with no per-seat subscription. Self-hosting trades the licence saving for infrastructure you have to run, so it pays off at sustained volume rather than for occasional use.
What is the best xAI alternative to Grok 4.6?
Grok 4 — same provider, same API surface, $2/1M input vs $2/1M. Strong coding value with 2M context — an underrated pick at this price.
Is Grok 4.6 still worth using in 2026?
Buy it for turn efficiency, not for benchmark ceilings.