ModelRank AI Coding Model Tiers

Compare coding capabilities by tier, with supporting evidence and API quotes when you need them.

25 models in this ranking Data checked:

Coding capability tiers

Tiers are editorial assessments. Models within each tier are alphabetical, with no implied capability order.

DeepSeek V4 Flash 0731 = 100. The index reflects two shared tests, not capability multiples or statistically established gaps.

Coding tier Model Coding IndexDifference from baseline Evidence Price (1M tokens) Context Details
S+Top-tier candidate · 1
S+ Claude Fable 5.1
anthropic
120.65 +20.65
Cross-checked
$10 / $50 per 1M OpenRouter quote
1,000,000 Details →
SFrontier coding group · 5
S Claude Fable 5
anthropic
114.18 +14.18
Cross-checked
$10 / $50 per 1M OpenRouter quote
1,000,000 Details →
S Kimi K3
moonshotai
113.02 +13.02
Cross-checked
$0.8 / $10 per 1M OpenRouter quote
1,048,576 Details →
S Claude Opus 5
anthropic
112.64 +12.64
Cross-checked
$5 / $25 per 1M OpenRouter quote
1,000,000 Details →
S GPT-5.6 Sol
openai
112.62 +12.62
Cross-checked
$2 / $10 per 1M OpenRouter quote
1,050,000 Details →
S GPT-6 Astra
openai
112.28 +12.28
Cross-checked
$10 / $50 per 1M OpenRouter quote
1,050,000 Details →
A+Near-frontier group · 9
A+ Gemini 3.8 Flash
google
111.92 +11.92
Cross-checked
$0.75 / $3.75 per 1M OpenRouter quote
1,048,576 Details →
A+ GLM-5.3
z-ai
111.83 +11.83
Cross-checked
$0.039 / $4.8 per 1M OpenRouter quote
1,048,576 Details →
A+ Gemini 3.7 Flash
google
111.28 +11.28
Cross-checked
$0.75 / $3.75 per 1M OpenRouter quote
1,048,576 Details →
A+ GPT-5.6 Terra
openai
110.54 +10.54
Cross-checked
$2 / $12 per 1M OpenRouter quote
1,050,000 Details →
A+ Grok 4.6
x-ai
108.55 +8.55
Cross-checked
$2 / $6 per 1M OpenRouter quote
500,000 Details →
A+ Claude Opus 4.8
anthropic
107.83 +7.83
Cross-checked
$5 / $25 per 1M OpenRouter quote
1,000,000 Details →
A+ Grok 4.5
x-ai
106.47 +6.47
Cross-checked
$2 / $6 per 1M OpenRouter quote
500,000 Details →
A+? DeepSeek V4.1 Flash
deepseek
—
Provisional · source review pending
$0.3 / $1.2 per 1M Official · peak Off-peak: $0.15 / $0.60
1,048,576 Details →
A+? Qwen3.8 Max 0902
qwen
—
Provisional · source review pending
$2 / $6 per 1M OpenRouter quote
1,000,000 Details →
AStrong coding group · 4
A Claude Sonnet 5
anthropic
105.06 +5.06
Cross-checked
$2 / $10 per 1M OpenRouter quote
1,000,000 Details →
A Qwen3.8-Next-Flash
qwen
104.89 +4.89
Cross-checked
$0.15 / $0.47 per 1M OpenRouter quote
1,000,000 Details →
A GLM-5.3-Flash
z-ai
104.81 +4.81
Cross-checked
$0.15 / $0.5 per 1M OpenRouter quote
1,048,576 Details →
A GPT-5.6 Luna
openai
104.63 +4.63
Cross-checked
$0.2 / $1.2 per 1M OpenRouter quote
1,050,000 Details →
B+Established reference group · 3
B+ DeepSeek V4 Pro 0813
deepseek
100.69 +0.69
Cross-checked
$1.32 / $3.96 per 1M Official · peak Off-peak: $0.66 / $1.98
1,048,576 Details →
B+ GLM-5.2
z-ai
100.32 +0.32
Cross-checked
$0.06 / $7 per 1M OpenRouter quote
1,048,576 Details →
B+ DeepSeek V4 Flash 0731
deepseek
Reference baseline = 100
100.00 0.00
Cross-checked
$0.44 / $1.32 per 1M Official · peak Off-peak: $0.22 / $0.66
1,310,720 Details →
BOther capable candidates · 2
B MiMo V2.5 Pro
xiaomi
91.23 -8.77
Single-source measurements
$0.435 / $0.87 per 1M OpenRouter quote
1,050,000 Details →
B HY3
tencent
88.93 -11.07
Single-source measurements
$0.132 / $0.528 per 1M OpenRouter quote
262,144 Details →
B-More basic within this selection · 1
B- MiMo V2.5
xiaomi
83.98 -16.02
Single-source measurements
$0.14 / $0.28 per 1M OpenRouter quote
1,050,000 Details →

Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.

Awaiting comparable evaluation (20)

Models awaiting tier review remain in the catalog. An unassigned tier does not mean weaker capability.

Compare current models

USD / API

API cost estimate

Estimated token cost

$400.0000
OpenRouter quote

Estimates use the displayed channel and time band; DeepSeek uses peak rates. Cache discounts, tool fees, long-context price tiers and uncounted additional tokens are excluded.

Ranking methodology

Only coding evidence determines the assessment. Measurements, known configurations and source dates remain available in the expanded notes; prices are separate purchasing information.

ModelRank assigns editorial tiers using public coding evidence, not automatic index thresholds or statistically proven classes. Price, speed, context size and sponsorship do not affect tiers.

Known model and agent configurations are retained with their sources. Unpublished evaluation dates, budgets, sample sizes and uncertainty remain unknown. Recent publication alone cannot rule out contamination.

Coding Index = 100 × the geometric mean of model/baseline ratios on AA Terminal-Bench 2.1 and SciCode, equally weighted. The baseline is fixed and displayed indices are integers. LiveBench and Terminal-Bench 4.0 provide supplementary checks; incomplete common coverage excludes them from this index.

100 × √[(TB / TB₀) × (SciCode / SciCode₀)]

Results reflect particular model configurations. Agent harnesses, reasoning effort, tools, budgets and tasks may differ. Close indices do not establish superiority. B describes a relative position in this selected group, not an inability to code.

ModelRank assigns editorial tiers using public coding evidence, not automatic index thresholds or statistically proven classes. Price, speed, context size and sponsorship do not affect tiers.

Qwen3.8 Max 0902 has a provisional A+ tier pending original-source review, with no numeric rank or comparable index. A transcribed AA index is not independent verification.

DeepSeek V4.1 Flash is provisionally A+ by editorial judgment, with low confidence. Early community feedback is mixed; the concrete coding comparison concerns GLM-5.3 Flash, not GLM-5.3. No comparable independent Coding Index is available.

Prefer recently collected tasks after a fixed model snapshot or well-governed private sets. A new benchmark name or a recent data fetch does not establish freshness. Require original sources, sample counts and test settings. Unknown task or evaluation dates remain unknown; recency alone cannot guarantee freedom from contamination.