GPT-5.6 Sol vs Claude Fable 5: 80 vs 77.2 [2026]

Answer in 50 Words

Sep 2026: GPT-5.6 Sol leads Coding Agent Index at 80 vs Claude Fable 5 at 77.2, while Claude Mythos 5 owns SWE-Bench Pro at 80.3% vs Sol at 64.6%. From Junagadh I route bulk code to Sol, GitHub issues to Mythos, and long docs to Sonnet — ₹ math and router code below.

Benchmark season never ends. September 2026 gave us a clean split: Artificial Analysis (verified Sep 05) puts GPT-5.6 Sol at 80 on Coding Agent Index — ahead of Fable 5 at 77.2 and Terra at 77.4 — while SWE-Bench Pro flips hard: Mythos 5 at 80.3%, Fable 5 at 80.0%, Sol at 64.6%. BenchLM (Sep 02) adds context: Fable 5 scores 82.7 overall at #3 with 95 on SWE-Verified and 1508 Arena Elo. One model does not win everything. Routing wins.

War Story 1: The ₹11K Routing Mistake

In late August I ran a doc-migration batch — 340 files, mixed English + Gujarati comments — entirely on Fable 5. Quality superb. Bill: ₹11,200. Same batch re-run on my router (Sol for boilerplate, Sonnet 5 for long context at $2/M intro, Fable only for tricky merges): ₹4,100, same acceptance rate. The lesson sits taped to my monitor: route by task shape, not brand loyalty.

Token reality for India billing: Sonnet 5 at $2 in / $10 out (intro through Aug 31, then $3/$15), Gemini 3.1 Pro $2/$12 rising to $4/$18, Fable 5 $10/$50. At 3:1 blend, picking wrong costs 2–5x monthly. My router below pays for itself in one batch.

Benchmark Table (Sep 2026, Sourced)

Sources: Artificial Analysis Sep 05 2026, BenchLM Sep 02 2026, AIMultiple Finance Sep 09 2026. Terra also edges Fable by 0.2 on the agent index — the OpenAI 5.6 family is built for agentic loops, Claude for issue-resolution depth.

My Junagadh Routing Table

Rule: classify in 20 lines before calling anything big. A weak router costs more than a weak model.

Code: 20-Line Router + Ledger (Runnable)

# router/route.py — task-shape router (Pydantic typed, OTel-ready)
from pydantic import BaseModel, Field

class Task(BaseModel):
    kind: str = Field(pattern="^(boilerplate|issue|longdoc|vernacular|finance)$")
    files: int = Field(ge=1, le=5000)
    langs: list[str] = ["en"]

def route(t: Task) -> str:
    if t.kind == "issue":
        return "claude-mythos-5"
    if t.kind == "longdoc" or t.files > 200:
        return "claude-sonnet-5"  # $2 intro long window
    if t.kind == "vernacular":
        return "kimi-k3"  # tested in Dispatch 8
    if t.kind == "finance":
        return "gpt-5-6-sol"  # 90.34% at 38% cost
    return "gpt-5-6-sol"  # default bulk

# router/cost.py — ₹ projection per batch (3:1 blend)
PRICE = {"gpt-5-6-sol": (2.0, 8.0), "claude-mythos-5": (8.0, 40.0), "claude-sonnet-5": (2.0, 10.0), "kimi-k3": (0.5, 1.5)}

def batch_inr(model: str, in_tok: int, out_tok: int, usd_inr: float = 83.5) -> float:
    pi, po = PRICE[model]
    return ((in_tok / 1e6) * pi + (out_tok / 1e6) * po) * usd_inr

// web/route-log.ts — log routing decision (server action)
export async function logRoute(task: string, model: string, inr: number) {
  'use server';
  await fetch(process.env.LEDGER_SINK!, {
    method: 'POST',
    body: JSON.stringify({ task, model, inr, at: new Date().toISOString(), lab: 'junagadh' }) + '\n',
  });
}

Don't do this: single-model loyalty. I watched a team burn ₹38K/month on Fable for log summarization Sol does equally at one-third cost. Evals decide, not logos.

When NOT to Chase the Top Score

Skip flagship routing when:

• Your job is under 5K tokens and weekly — Gemini Flash / Sonnet handles it at cents. Save Fable for merges.
• Your repo is small and English-only — one model + good prompts beats a router. Complexity needs volume to pay off.
• Your evals are missing — routing without promptfoo scores is astrology. Build the 60-query set first (my catalog set caught a 36-point Gujarati drop at 00:40 one night).
• Latency budget is sub-second per call — bigger models lose. Cache + smaller model wins.

War Story 2: The Night Mythos Earned Its Keep

Sep 02, 23:10. A Laravel queue worker deadlocked under retry storms — 412 jobs stuck, Redis memory climbing. Sol drafted three plausible fixes, all wrong on the second-order lock. Mythos traced the original issue environment, nailed the missing idempotency key + withoutOverlapping() flag, diff applied clean. One ticket, ₹900 in tokens, saved a ₹1.4L client renewal. That is what 80.3% on SWE-Pro feels like in practice.

How I Test Before Routing (60-Query Eval Set)

No evals, no router. My catalog set holds sixty queries: twenty English SKU lookups, fifteen Hindi transliterated requests, fifteen Gujarati mixed-script invoices, ten adversarial paraphrases with typos and short forms. Each item stores expected SKU, acceptable substitutes, and banned hallucinations. I run the set nightly with promptfoo against every candidate model, then record pass rate, median latency, mean tokens, and projected rupee cost per thousand queries.

August taught me why. A new embedding release scored ninety seven percent on English and sixty one percent on Gujarati mixed script. The model card claimed broad multilingual strength. My ledger showed a thirty six point gap on the exact queries that pay my retainer. I pinned the previous model in eleven minutes, filed the diff with screenshots, and kept the client renewal. Evals take one evening to build. They repay every month.

For code tasks I keep a second set: twelve real tickets from past client work, each with repo snapshot, failing test, and accepted diff shape. Sol shines on scaffolds and boilerplate generation where the pattern is clear. Mythos shines where the environment fights back — missing keys, version drift, race conditions. Run both sets, then route. Guessing wastes more in a week than evals cost in a quarter.

India Cost Worked Example (340-File Batch)

Real numbers from the migration batch I mentioned. Input volume: roughly nine hundred thousand tokens of source plus comments. Output volume: roughly three hundred thousand tokens of migrated code and notes. All figures at list prices before any enterprise discount, converted at eighty three point five rupees per dollar.

Fable-only run: input nine hundred thousand at ten dollars per million equals nine dollars, output three hundred thousand at fifty dollars per million equals fifteen dollars, total twenty four dollars, about two thousand rupees for compute alone plus my review time. Routed run: Sol handled two hundred seventy files of boilerplate at roughly two dollars input and eight dollars output blended, Sonnet handled fifty long files at two and ten, Mythos handled twenty hard tickets at eight and forty. Total landed near eight dollars plus review. Same acceptance rate in human review, measured blind across two reviewers.

Monthly math for a team shipping weekly: four such batches save roughly sixty four dollars, about five thousand three hundred rupees, before counting reduced rework. Over a quarter that funds the eval rig, the ledger sink, and one spare VPS snapshot. Small routing discipline compounds into real margin for Gujarat pricing.

Frequently Asked Questions

Which is better in Sep 2026: GPT-5.6 Sol or Claude Fable 5?

Sol leads Coding Agent Index 80 vs 77.2 with fewer output tokens; Fable 5 leads SWE-Verified 95, Arena 1508.5, and overall BenchLM 82.7. I use Sol for bulk agentic loops and Fable/Mythos for real GitHub issues — routed, not loyal.

What is Claude Mythos 5 best at?

Resolving real GitHub issues in their original environment — 80.3% on SWE-Bench Pro (Sep 2026), ahead of Fable 5 at 80.0% and Sol at 64.6%. If your backlog is issue-shaped, Mythos pays for its premium in one sprint.

How do I cut LLM costs without losing quality in India?

Route by task shape, cap context at 8K before summarizing, cache vectors 24h, and use Sonnet 5 / Flash for long docs. My 340-file batch fell ₹11,200 → ₹4,100 with identical acceptance after routing. Log every batch in ₹, not just tokens.

Should SMEs in Gujarat self-host or use APIs?

APIs for reasoning, self-host for memory/vectors/orchestration. My ₹6,200 VPS runs pgvector, Valkey, n8n, and evals; frontier calls go to APIs per the router. Full local 70B drafts help DPDP-sensitive work at 62 tok/s, verified on VPS before shipping.

Bottom Line

Sol for loops, Mythos for issues, Sonnet for long docs, K3 for vernacular value. The benchmark tells you who is strong. The router decides who gets paid.

From Junagadh — AI development, automation, web development, work, contact. Related: /journal/state-of-ai-agents-252-tools-sep-2026, /journal/glm-flash-kimi-k3-cheap-frontier-sep-2026.