Best LLM for Agentic Coding 2026: Ranked by Cost
The Short Answer
Ranked by cost per completed task rather than benchmark score, because that is the number that shows up on your invoice:
- Muse Spark 1.3 — best value at $1.72/task, index 64.2
- GPT-6 Astra — best quality-per-dollar at the top, $3.27/task, index 67.0
- Claude Fable 5.1 — highest quality, $9.18/task, index 70.4
- DeepSeek V4 Pro — best open-weight frontier, from $0.66/$1.98 per MTok
- GPT-5.6 Luna — cheapest usable, $0.29/task, index 57.1
Last verified: September 4, 2026.
The Ranking Table
Artificial Analysis coding-agent harness, September 2026. Cost per task is measured, not calculated from list prices.
| Rank | Agent · Model | Coding Agent Index | Cost per task |
|---|---|---|---|
| 1 (value) | Muse Code · Muse Spark 1.3 (xhigh) | 64.2 | $1.72 |
| 2 | Codex · GPT-6 Astra (xhigh) | 67.0 | $3.27 |
| 3 (quality) | Claude Code · Fable 5.1 (max) | 70.4 | $9.18 |
| 4 | Claude Code · Opus 5 (xhigh) | 68.1 | $8.17 |
| 5 | Codex · GPT-6 Astra (medium) | 65.1 | $2.19 |
| 6 | Codex · GPT-5.6 Sol (max) | 65.0 | $5.00 |
| 7 | Codex · GPT-6 Astra (low) | 62.6 | $1.41 |
| 8 | Opencode · Gemini 3.8 Flash (high) | 61.1 | $2.04 |
| 9 | Opencode · Gemini 3.7 Flash (high) | 59.6 | $1.27 |
| 10 | Codex · GPT-5.6 Luna (max) | 57.1 | $0.29 |
The shape to notice: the top of the table costs 5x the middle for about 6 index points. Whether those points matter is entirely a function of what your agent does when it fails.
1. Muse Spark 1.3 — Best Value
$1.25 / $4.25 per MTok · $1.72 per task · index 64.2
Meta’s September 2, 2026 release beats Gemini 3.8 Flash on both quality and cost per task — the only mid-tier model on the board to manage it. Published figures: 75.4% on DeepSWE v1.1 (ahead of Claude Opus 5 at 74.0 and GPT-5.6 Sol at 73.0), 88.8% on Terminal-Bench 2.1, and 98.5% / 98.1% on the MRCR long-context bands where GPT-5.6 Sol drops to 73.8%.
The long-context retrieval is the differentiator for codebase-wide work. Meta also measured roughly 20% fewer tool calls and 25% fewer tokens than Muse Spark 1.2.
Limits: text input only, max reasoning mode still in safety testing, open weights promised but unshipped, and no independent per-benchmark verification at launch.
2. GPT-6 Astra — Best Quality-per-Dollar Near the Top
$10 / $50 per MTok · $3.27 per task · index 67.0
The expensive model that is cheap in practice. Astra emits roughly 2,200–14,000 output tokens per task where Fable 5.1 needs 14,500–45,000, so a 13x price premium over Flash tiers converts into a lower bill. Every effort level sits on the Artificial Analysis Pareto frontier.
It also leads abstract reasoning decisively: 62.7% on ARC Prize’s official standard ARC-AGI-3 harness against 30.2% for Opus 5.
Limits: staged rollout via enterprise Trusted Access first, no fine-tuning or realtime audio at launch, and prompts above 272,000 tokens reprice at a long-context premium of roughly 2x input.
3. Claude Fable 5.1 — Highest Quality
$10 / $50 per MTok, cache reads $0.25 · $9.18 per task · index 70.4
The best measured agentic coding model available, and the numbers are official and reproducible rather than leaked: 55.8% on Terminal-Bench 4.0 (60.9% as Mythos 5.1), 73.4% on CursorBench 3.2.0, 52.6% on Terminal-Bench Science 0.1 — more than double Fable 5’s 24.7% — 77.9% partial on OSWorld 2.0, and 1853 on GDPval-AA v2.
The $0.25 cache reads are the underrated feature. Long agent runs re-read accumulated context constantly, and Anthropic estimates typical workloads land about 25% cheaper than Fable 5 with highly agentic ones up to roughly 45% cheaper. That partly offsets the headline per-task cost for context-heavy loops.
GA on AWS, Google Cloud, Microsoft Azure and the Anthropic API from day one.
Limits: the price. At roughly 5x Muse Spark 1.3 per task, it needs to be earning its keep on genuinely hard work.
4. The Open-Weight Tier
If self-hosting, data residency or an absolute cost floor drives the decision, the Chinese frontier labs changed the math in 2026.
| Model | Released | Input / Output per MTok | License |
|---|---|---|---|
| DeepSeek V4 Pro | Aug 13, 2026 GA | $0.66 / $1.98 off-peak (2x peak) | MIT |
| Kimi K3 | Jul 16, 2026 | $3.00 / $15.00 | Kimi K3 licence |
| GLM 5.3 | Aug 2026 | $1.09–$1.40 / $3.43–$4.40 | staged |
| GLM 5.3 Flash | Aug 26, 2026 | $0.071 / $0.238 | staged |
| DeepSeek V4 Flash | Jul 31, 2026 | $0.22 / $0.66 off-peak | MIT |
| Qwen 3.8 Flash | Aug 24, 2026 | $0.15 / $0.47 | open weights |
DeepSeek V4 Pro tops LiveCodeBench at 93.5, posts a Codeforces ELO of 3206 against 3168 for GPT-5.5, and statistically ties Claude Opus 4.7 on SWE-bench Verified at 80.6 vs 80.8. Kimi K3 ranks number 4 of 189 on the Artificial Analysis Intelligence Index at 57 — the highest position an open-weights model has recorded — and holds number 1 on the Arena frontend coding leaderboard at 1,679 Elo across 483,895 blind votes.
DeepSeek’s time-of-day billing is unique: peak rates apply 01:00–04:00 and 06:00–10:00 UTC on weekdays only, so off-peak covers roughly 79% of the week including all weekends, at exactly half price. Cache hits are near-free at $0.022 per million on V4 Pro.
Limits: the largest checkpoints need rack-scale hardware to self-host, vendor benchmark results often use the vendor’s own harness at max effort, and hosted Chinese endpoints raise data-residency questions that MIT weights let you sidestep by hosting yourself.
5. GPT-5.6 Luna — Cheapest Usable
$0.20 / $1.20 per MTok · $0.29 per task · index 57.1
The cheapest thing on the board that still completes real coding tasks. At index 57.1 it is roughly 13 points behind Fable 5.1 — a large gap — but it costs 1/32nd as much per task. For boilerplate, test scaffolding, simple refactors and high-volume mechanical edits, that trade is often correct.
How to Actually Choose
Route, do not standardise. The strongest 2026 pattern is classifying each request rather than picking one model. Bounded work — boilerplate, tests, mechanical refactors, code search — to a Flash or Luna tier. Open-ended debugging, architecture and anything where “figure out why this is broken” is the task — to a frontier model. Teams doing this land the majority of volume on the cheap tier.
Measure on your own backlog. Run a hundred representative tickets, record input tokens, output tokens, cached reads, tool calls and a success flag, then compute total spend divided by successfully completed tasks. Token consumption depends on your workload as much as on the model, so leaderboards cannot answer this for you.
Sweep the effort dial. GPT-6 Astra ranges from $1.41 to $4.72 per task across its effort levels. The best cost-per-task point is rarely the highest setting.
Keep the model behind a config flag. Tier swaps should be a deploy. This also protects against scheduled repricing — Gemini Flash’s introductory rate doubles on January 1, 2027, and promotional pricing is now the norm rather than the exception.