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ModelRumors.ai FRONTIER AI BUYER'S GUIDE

EFFORT-LEVEL EQUIVALENCE & WORKLOAD SELECTION GUIDE · SOURCED DATA (2026-11-05)

Compare Frontier Models by Reasoning Effort & Use Case

Compare frontier models across reasoning-effort settings and workloads. Every number links to its source, and scores name the effort level only where the source does.

Highest GPQA Diamond we track
Gemini 4 Argon
96.4% (Epoch AI) source ↗
Top Arena Text score you can use today
Gemini 4 Argon
1520 on Arena Text (Oct 27, 2026) source ↗
Fastest output we track
DeepSeek V4.1-Flash
174 tok/s (Artificial Analysis) source ↗
Lowest list price we track
DeepSeek V4.1-Flash
$0.15 / $0.60 per 1M off-peak source ↗

FRONTIER FLAGSHIP PER LAB

Each lab's flagship model

The model each lab leads with today, its reasoning-effort controls and its sourced evaluations.

LAB FRONTIER FLAGSHIP LIST PRICE ($/1M) EFFORT TIERS & API PARAMETER WHAT IT IS BEST AT & SOURCED EVALS
OpenAI GPT-6 Astra ↗
Released 2026-09-03
$10 / $50 / 1M ↗ low · medium · high · xhigh · max
reasoning={"effort": "low" | "medium" | "high" | "xhigh" | "max"}
OpenAI's most capable model: 96.0% GPQA Diamond, 74.1% DeepSWE v1.1 and 57.9% Terminal-Bench 4.0 (OpenAI).
DeepSWE v1.1: 74.1% (OpenAI) ↗ · Terminal-Bench 4.0: 57.9% (OpenAI) ↗ · AA Intelligence Index v5.0.1: 57 (Max) (Artificial Analysis) ↗
Anthropic Claude Opus 5.5 ↗
Released 2026-09-22
$4 / $20 / 1M ↗ low · medium · high · xhigh · max
output_config={"effort": "low" | "medium" | "high" | "xhigh" | "max"}
Anthropic suggests starting with Opus 5.5 for most workloads. #1 on Arena WebDev (1810); 66.4% on Terminal-Bench 4.0 at xhigh effort (Anthropic).
Arena WebDev: 1810 (#1) (Arena) ↗ · SWE-bench Pro: 89.9% (Anthropic system card) ↗ · Terminal-Bench 4.0: 66.4% (xhigh) (Anthropic) ↗ · GDPval-AA v2.1: 1846 (Anthropic) ↗ · CursorBench 4.0: 57.8% at max effort, 52.5% at default (Anthropic) ↗ · AA Intelligence Index v5.0.1: 59 (Artificial Analysis) ↗
Google DeepMind Gemini 4 Argon ↗
Released 2026-10-13
$2 / $10 / 1M ↗ low · medium · high
thinking_config={"thinking_level": "low" | "medium" | "high"}
Highest GPQA Diamond score we track (96.4%, Epoch AI) and the top Arena Text score among models you can use today (1520).
DeepSWE v1.1: 77.9% (Google) ↗ · AA Intelligence Index v5.0.1: 50 (High) (Artificial Analysis) ↗
xAI Grok 4.7 ↗
Released 2026-09-21
$2 / $6 / 1M ↗ low · medium · high · xhigh
reasoning_effort="low" | "medium" | "high" | "xhigh"
xAI's flagship. Reasoning is always on and cannot be turned off; 500K context; 71.0% on DeepSWE v1.1 (xAI).
DeepSWE v1.1: 71.0% (xAI) ↗ · CursorBench 4.0: 46.3% (xAI) ↗ · AA Intelligence Index v5.0.1: 35 (xhigh) (Artificial Analysis) ↗
DeepSeek DeepSeek V4.1-Flash ↗
Released 2026-09-10
$0.15 / $0.60 / 1M ↗ low · high · max
thinking={"type": "enabled" | "disabled"}, reasoning_effort="low" | "high" | "max" (medium and xhigh map to high)
Fastest output we track (174 tok/s, Artificial Analysis) and the lowest list price ($0.15 / $0.60 off-peak); MIT open weights.
DeepSWE v1.1: 77.0% (Oct 17 update; 74.2% on Sep 10) (DeepSeek) ↗
Meta Muse Wonder ↗
Released 2026-10-12
$4 / $20 / 1M ↗ minimal · low · medium · high · xhigh · max
reasoning_effort="minimal" | "low" | "medium" | "high" | "xhigh" | "max" (Chat Completions)
Meta's highest-ranked model on Arena Text (1507, #4 at Max effort). The API is in preview.
Arena Text: 1507 (#4, Max effort) (Arena) ↗

INTERACTIVE WORKLOAD & SDK CONFIG RECOMMENDER

Pick your workload and constraint to get the exact model, effort tier & SDK snippet

Select a workload category and budget/weights policy below. The recommender outputs the primary frontier pick, its exact reasoning-effort API syntax, and a cross-lab fallback in case your primary provider is past its typical release gap.

INTERACTIVE EFFORT BREAK-EVEN CALCULATOR · THINKING TOKEN MATH

How many extra reasoning tokens can a cheaper model spend at high or max effort before it costs as much as a flagship?

Reasoning models bill thinking tokens at the output rate. Pick two models and set how many times more output tokens the challenger uses. That multiplier is your assumption, so measure it on your own requests. The calculator shows the cost per 1M base tokens and the break-even multiplier at today's list prices.

REASONING EFFORT & THINKING BUDGET COMPARISONS · SOURCED BENCHMARKS

Compare models by effort level (low · medium · high · xhigh · max)

Standalone Effort & Use-Case Guide →

Every lab exposes a reasoning-effort or thinking-level setting. Higher effort spends more output tokens. Scores below name the effort level only where the source does, and the evaluator is named for every score.

GPT-6 Astra or GPT-6.1 Sol: how do they compare?

Sol: near-Astra at one-fifth of the price (OpenAI)
GPT-6 Astra · effort: low to max
List price: $10 / $50 per 1M · API: reasoning={"effort": "high"}
GPT-6.1 Sol · effort: low to max (default medium)
List price: $2 / $10 per 1M · API: reasoning={"effort": "max"}

OpenAI describes GPT-6.1 Sol as near-Astra in performance at lower cost: $2 / $10 per 1M input/output tokens against $10 / $50 for GPT-6 Astra, for prompts up to 272K tokens. On Arena Text, both rated at Max effort, Sol scores 1481 and Astra 1476, a gap too small to call. Artificial Analysis scores Astra 57 and Sol 55 on its Intelligence Index (Max). OpenAI publishes DeepSWE v1.1 (74.1%) and Terminal-Bench 4.0 (57.9%) for Astra, and no comparable scores for Sol. Reasoning tokens bill as output, so higher effort costs more; the break-even calculator shows how many more output tokens Sol can use before it costs as much as Astra.

Sources: OpenAI GPT-6.1 Sol docs ↗ · OpenAI GPT-6 Astra ↗ · Arena Text ↗ · Artificial Analysis: Astra ↗ · OpenAI API pricing ↗

Which models match GPT-6 Astra on science and coding benchmarks?

Several models match Astra on GPQA Diamond for less
GPT-6 Astra · effort: max
List price: $10 / $50 per 1M · API: reasoning={"effort": "max"}
Other labs · effort: as each source reports
List price: see below · API: see each model

GPQA Diamond (graduate science): Gemini 4 Argon 96.4% (Epoch AI), GPT-6 Astra 96.0% (reported by OpenAI), Claude Sonnet 5.5 95.6% (Epoch AI, max effort) and Gemini 3.8 Flash 95.4% (Epoch AI). The evaluators differ, so treat gaps under a point as ties. Coding and agents: OpenAI reports 57.9% on Terminal-Bench 4.0 for Astra; Anthropic reports 66.4% for Claude Opus 5.5 at xhigh effort. Artificial Analysis Intelligence Index v5.0.1: Opus 5.5 59, Astra 57 (Max), Sol 55 (Max), Argon 50 (High). Prices per 1M input/output: Astra $10 / $50, Opus 5.5 $4 / $20, Argon $2 / $10, Sonnet 5.5 $2 / $10, 3.8 Flash $0.75 / $3.75.

Sources: Epoch AI GPQA Diamond ↗ · OpenAI GPT-6 Astra ↗ · Anthropic Claude Opus 5.5 ↗ · Artificial Analysis: Astra ↗

Claude Opus 5.5 or Claude Sonnet 5.5, and where does Fable 5.1 fit?

Opus 5.5 for coding · Sonnet 5.5 (max) for GPQA science
Claude Opus 5.5 · effort: medium (default) to xhigh
List price: $4 / $20 per 1M · API: output_config={"effort": "xhigh"}
Claude Sonnet 5.5 · effort: max
List price: $2 / $10 per 1M · API: output_config={"effort": "max"}

Anthropic suggests starting with Claude Opus 5.5 for most workloads; its API default effort is medium. Opus 5.5 leads Sonnet 5.5 on SWE-bench Pro (89.9% vs 81.3%, Anthropic system cards), ranks #1 on Arena WebDev (1810) and scores 1509 vs 1477 on Arena Text. On GPQA Diamond, run by Epoch AI at max effort, Sonnet 5.5 scores 95.6% against 90.6% for Opus 5.5, streams faster (155 vs 110 tok/s, Artificial Analysis) and costs half as much ($2 / $10 vs $4 / $20). Anthropic calls Claude Fable 5.1 ($10 / $50) its most capable generally available model, for demanding reasoning and long-horizon agentic work; on Terminal-Bench 4.0, Anthropic reports Opus 5.5 at 66.4% and Fable 5.1 at 55.8%.

Sources: Anthropic Claude Opus 5.5 ↗ · Opus 5.5 system card ↗ · Sonnet 5.5 system card ↗ · Epoch AI GPQA Diamond ↗ · Anthropic effort docs ↗ · Claude Fable ↗ · Fable 5.1 docs ↗

Gemini 4 Argon or Gemini 3.8 Flash?

3.8 Flash: 99% of Argon's GPQA score at 38% of the price
Gemini 4 Argon · effort: low to high (default medium)
List price: $2 / $10 per 1M · API: thinking_level="high"
Gemini 3.8 Flash · effort: low to high (default medium)
List price: $0.75 / $3.75 per 1M · API: thinking_level="high"

On Epoch AI's GPQA Diamond, Gemini 3.8 Flash scores 95.4% against 96.4% for Gemini 4 Argon, and Google reports 73.7% against 77.9% on DeepSWE v1.1. 3.8 Flash streams at 117 tok/s against 74 (Artificial Analysis) and costs $0.75 / $3.75 against $2 / $10 per 1M. On Arena Text (High thinking), Argon scores 1520 and 3.8 Flash 1496 (preliminary). Scheduled changes: Gemini 4 Argon becomes $4 / $20 on Dec 1, 2026; Gemini 3.8 Flash becomes $1.5 / $7.5 on Jan 1, 2027.

Sources: Epoch AI Gemini 4 Argon ↗ · Epoch AI Gemini 3.8 Flash ↗ · Gemini 4 Argon model card ↗ · Google Gemini API pricing ↗

DeepSeek V4.1-Flash or DeepSeek V4-Pro?

V4.1-Flash: same Arena tier, faster and cheaper · V4-Pro: higher GPQA
DeepSeek V4.1-Flash · effort: low, high (default) or max
List price: $0.15 / $0.60 off-peak · API: reasoning_effort="max"
DeepSeek V4-Pro · effort: low, high (default) or max
List price: $0.66 / $1.98 off-peak · API: reasoning_effort="max"

On Arena Text, V4.1-Flash (Sep 10 version) scores 1476 ±6 and V4-Pro-0813 scores 1465 ±7, a gap inside the margin of error. V4-Pro scores higher on GPQA Diamond (92.4% vs 90.9%, DeepSeek). The only coding test both have is SWE-bench Verified from the April V4 preview cards, at Max: V4-Pro 80.6%, V4-Flash 79.0%. The Oct 17 V4.1-Flash update reports 77.0% on DeepSWE v1.1. Off-peak prices per 1M: V4.1-Flash $0.15 / $0.60, V4-Pro $0.66 / $1.98, so Flash costs 23% to 30% as much. DeepSeek says V4-Pro API service continues until V4.1-Pro launches.

Sources: DeepSeek V4.1-Flash-1017 ↗ · DeepSeek V4-Pro-0813 ↗ · DeepSeek V4 preview (April) ↗ · DeepSeek V4.1-Flash card (GPQA) ↗ · DeepSeek API pricing ↗ · Arena Text ↗

API Effort Controls & Evaluated Benchmark Tiers Across All 12 Models

ModelSupported Effort TiersDefault / Benchmark TierAPI Parameter SyntaxList $ / 1M
Gemini 4 Argon (Google DeepMind)low · medium · highmedium / highthinking_config={"thinking_level": "low" | "medium" | "high"}$2 / $10 source ↗
Claude Opus 5.5 (Anthropic)low · medium · high · xhigh · maxmedium / maxoutput_config={"effort": "low" | "medium" | "high" | "xhigh" | "max"}$4 / $20 source ↗
Muse Wonder (Meta)minimal · low · medium · high · xhigh · maxnot stated / maxreasoning_effort="minimal" | "low" | "medium" | "high" | "xhigh" | "max" (Chat Completions)$4 / $20 source ↗
Claude Fable 5.1 (Anthropic)low · medium · high · xhigh · maxhigh / not statedoutput_config={"effort": "low" | "medium" | "high" | "xhigh" | "max"}$10 / $50 source ↗
Gemini 3.8 Flash (Google DeepMind)low · medium · highmedium / highthinking_config={"thinking_level": "low" | "medium" | "high"}$0.75 / $3.75 source ↗
GPT-6.1 Sol (OpenAI)low · medium · high · xhigh · maxmedium / maxreasoning={"effort": "low" | "medium" | "high" | "xhigh" | "max"}$2 / $10 source ↗
Claude Sonnet 5.5 (Anthropic)low · medium · high · xhigh · maxhigh / maxoutput_config={"effort": "low" | "medium" | "high" | "xhigh" | "max"}$2 / $10 source ↗
GPT-6 Astra (OpenAI)low · medium · high · xhigh · maxnot stated / maxreasoning={"effort": "low" | "medium" | "high" | "xhigh" | "max"}$10 / $50 source ↗
DeepSeek V4.1-Flash (DeepSeek)low · high · maxhigh / not statedthinking={"type": "enabled" | "disabled"}, reasoning_effort="low" | "high" | "max" (medium and xhigh map to high)$0.15 / $0.60 source ↗
DeepSeek V4-Pro (DeepSeek)low · high · maxhigh / maxthinking={"type": "enabled" | "disabled"}, reasoning_effort="low" | "high" | "max" (medium and xhigh map to high)$0.66 / $1.98 source ↗
Grok 4.7 (xAI)low · medium · high · xhighhigh / xhighreasoning_effort="low" | "medium" | "high" | "xhigh"$2 / $6 source ↗
Muse Spark Open (Meta)not verifiednot stated / not statedNot verified: reasoning controls depend on the host$1.25 / $4.25 source ↗

USE-CASE SELECTION MATRIX · WHAT EACH FRONTIER MODEL IS BEST AT

Which model and effort level should you pick for your workload?

No single model wins every category. Below is our workload-by-workload breakdown built strictly from published Arena, Epoch AI, Artificial Analysis, and official model-card evaluations.

WORKLOAD SELECTION GUIDE

1. Multi-File Agentic Coding, Terminal Tasks & WebDev UI

BEST OVERALL Claude Opus 5.5 (xhigh effort)
BEST VALUE / SPEED GPT-6.1 Sol (high effort)
BEST OPEN WEIGHTS DeepSeek V4.1-Flash (max)

Anthropic reports Claude Opus 5.5 at 66.4% on Terminal-Bench 4.0 (xhigh), 57.8% on CursorBench 4.0 (max effort; 52.5% at its default effort) and 89.9% on SWE-bench Pro, and it ranks #1 on Arena WebDev (1810). OpenAI describes GPT-6.1 Sol as near-Astra at $2 / $10 per 1M. Google reports 77.9% on DeepSWE v1.1 for Gemini 4 Argon ($2 / $10). Among open weights, DeepSeek V4.1-Flash-1017 reports 77.0% on DeepSWE v1.1 at $0.15 / $0.60 off-peak.

Sources: Anthropic Opus 5.5 ↗ · Opus 5.5 system card ↗ · OpenAI GPT-6.1 Sol docs ↗ · Gemini 4 Argon card ↗ · DeepSeek V4.1-Flash-1017 ↗
WORKLOAD SELECTION GUIDE

2. Graduate-Level Science, Quantitative Research & Math (GPQA Diamond)

BEST OVERALL Gemini 4 Argon (high thinking) · 96.4% GPQA
BEST VALUE / SPEED Gemini 3.8 Flash (high thinking) · 95.4% GPQA at $0.75 / $3.75
BEST OPEN WEIGHTS DeepSeek V4-Pro (max) · 92.4% GPQA

GPQA Diamond: Gemini 4 Argon 96.4% (Epoch AI), GPT-6 Astra 96.0% (reported by OpenAI), Claude Sonnet 5.5 95.6% (Epoch AI, max effort) and Gemini 3.8 Flash 95.4% (Epoch AI). Sonnet 5.5 outscores Opus 5.5 (90.6%) here. Among open weights, DeepSeek reports 92.4% for V4-Pro.

Sources: Epoch AI Gemini 4 Argon ↗ · OpenAI GPT-6 Astra ↗ · Epoch AI GPQA Diamond ↗ · DeepSeek V4.1-Flash card ↗
WORKLOAD SELECTION GUIDE

3. General Human-Preference Writing, Synthesis & Hard Prompts (Arena Text)

BEST OVERALL Gemini 4 Argon (1520) or Claude Opus 5.5 (1509)
BEST VALUE / SPEED Gemini 3.8 Flash (1496, preliminary) at $0.75 / $3.75
BEST OPEN WEIGHTS DeepSeek V4.1-Flash (1476, MIT)

On Arena Text (Oct 27, 2026), four models you can use today score above 1500: Gemini 4 Argon (1520, High), Claude Opus 5.5 (1509), Muse Wonder (1507, Max) and Claude Fable 5.1 (1504). Google's gated Gemini 4 Carbon shows 1528 (preliminary). Gemini 3.8 Flash scores 1496 (preliminary) and DeepSeek V4.1-Flash 1476.

Sources: Arena Text Leaderboard ↗
WORKLOAD SELECTION GUIDE

4. Occupational Knowledge Work, Financial Analysis & Long Documents (1M Context)

BEST OVERALL Claude Opus 5.5 (1846 on GDPval-AA v2.1)
BEST VALUE / SPEED GPT-6.1 Sol (1.05M context)
BEST OPEN WEIGHTS Muse Spark Open (1M context)

Anthropic reports Claude Opus 5.5 at 1846 on GDPval-AA v2.1 (44 occupations), against 1735 for Fable 5.1. GPT-6.1 Sol has a 1.05M-token context at $2 / $10 per 1M for prompts up to 272K tokens and $4 / $15 above. Muse Spark Open has a 1M context window and open weights.

Sources: Anthropic Opus 5.5 ↗ · OpenAI GPT-6.1 Sol docs ↗ · OpenAI API pricing ↗ · Meta Muse Spark Open ↗
WORKLOAD SELECTION GUIDE

5. High-Throughput Production Chat, Tool Calling & Sub-Second Loops

BEST OVERALL Claude Sonnet 5.5 (low effort)
BEST VALUE / SPEED DeepSeek V4.1-Flash (low) at $0.15 / $0.60 off-peak
BEST OPEN WEIGHTS DeepSeek V4.1-Flash (low)

Artificial Analysis measures DeepSeek V4.1-Flash-1017 at 174 tok/s, Claude Sonnet 5.5 at 155, Muse Spark Open at 153 (varies by host), Gemini 3.8 Flash at 117 and Claude Opus 5.5 at 110. Lower effort means fewer reasoning tokens before the answer; Anthropic's effort guide describes lower effort as faster and cheaper for simpler tasks.

Sources: AA DeepSeek V4.1-Flash ↗ · AA Claude Sonnet 5.5 ↗ · AA Muse Spark Open ↗ · Anthropic effort docs ↗
WORKLOAD SELECTION GUIDE

6. Open-Weights Self-Hosting, On-Prem Privacy & Custom Fine-Tuning

BEST OVERALL DeepSeek V4.1-Flash or V4-Pro (MIT License)
BEST VALUE / SPEED DeepSeek V4.1-Flash ($0.15 / $0.60 API, or self-host under MIT)
BEST OPEN WEIGHTS DeepSeek V4.1-Flash (MIT) · Muse Spark Open (Muse Model License) · Muse Glimmer 30B (Apache 2.0)

DeepSeek publishes V4.1-Flash and V4-Pro weights under the MIT license, with a 1M context and a setting to turn thinking on or off. Meta's Muse Spark Open (816B total, 42B active, 1M context, 89.0% GPQA Diamond) uses the Muse Model License with limited commercial terms; Muse Glimmer 30B is Apache 2.0.

Sources: DeepSeek V4.1-Flash card ↗ · DeepSeek thinking mode ↗ · Meta Muse Spark Open ↗ · Meta Muse Glimmer 30B ↗

SCHEDULED PRICE STEP-UPS, PROMO EXPIRATIONS & DEPRECATION RADAR

Upcoming dates that change your API bill or model routing

Subscribe via RSS / Atom (/feed.xml) →

Raw model registries show today's rate without warning you when an introductory promo expires or an endpoint is retiring. Every date below links to the provider's own pricing, deprecation or news page.

2026-12-01 · Google DeepMind · Gemini 4 Argon
PRICE STEP-UP (2X)
Introductory $2.00 / $10.00 per 1M rate ends Nov 30, 2026; the standard rate becomes $4.00 / $20.00 per 1M on Dec 1, 2026. source ↗
Recommended action: Budget twice the Gemini 4 Argon token spend from Dec 1, or compare Gemini 3.8 Flash ($0.75 / $3.75 through Dec 31) and Claude Sonnet 5.5 ($2 / $10).
2026-11-30 · OpenAI · GPT-5.6 Sol
PROMO ENDS
GPT-5.6 Sol's promotional $4.00 / $20.00 per 1M price runs until Nov 30, 2026. source ↗
Recommended action: Compare GPT-6.1 Sol ($2 / $10 per 1M for prompts up to 272K tokens) before the promotion ends.
2027-01-01 · Google DeepMind · Gemini 3.8 Flash
PRICE STEP-UP (2X)
Promotional $0.75 / $3.75 per 1M rate runs through Dec 31, 2026; it becomes $1.50 / $7.50 per 1M on Jan 1, 2027. source ↗
Recommended action: Finish cost-sensitive batch runs before Dec 31, 2026, and re-compare against GPT-6.1 Terra ($1 / $5) and DeepSeek V4.1-Flash ($0.15 / $0.60 off-peak) in January.
When V4.1-Pro ships · DeepSeek · DeepSeek V4-Pro (0813)
API ROUTING TRANSITION
DeepSeek stated on Sep 10, 2026 that V4-Pro API service continues until DeepSeek V4.1-Pro launches (no date announced). source ↗
Recommended action: Use deepseek-flash (V4.1-Flash-1017) for coding and general tasks, or self-host the MIT weights if you need pinned V4-Pro-0813 behavior.
2027-04-01 · OpenAI · Older GA snapshots
DEPRECATION NOTICE
OpenAI announced its next retirement wave for Apr 1, 2027, about six months ahead. source ↗
Recommended action: Check OpenAI's deprecation table for pinned snapshots and plan migrations before Apr 1, 2027.
2027-10-07 · Anthropic · Retirement dates
RETIREMENT DATES
Anthropic lists a 'not sooner than' retirement date 12 months after each model's release, for example Oct 7, 2027 for Claude Haiku 5.5. source ↗
Recommended action: Pin model IDs in production and review Anthropic's deprecation table each quarter.