Model shortlist

Best coding model APIs for agents and code review

Compare coding model APIs by context length, tools, JSON output, latency, price, and what role they should play in production.

What is this shortlist for?: Coding models

A model that completes a 20-line function is not the same product as a model that reads half a monorepo and calls tools. Output is expensive, tool calls fail in boring ways, and long context burns money. Use this page to pick a primary coding model and a cheaper fallback, then decide budget rules before agents run unsupervised.

Source basis: NextModel use-case taxonomy and OpenRouter supported-parameter metadata when available. · Updated 2026-07-01

How to use this shortlist

How to use this shortlist (Coding models)

  1. Match the shortlist to the job. Check whether the Coding models candidates fit your real workload, not only the posted rate.
  2. Run the same prompts. Test two or three candidates on production-like prompts and note quality and output length.
  3. Estimate monthly cost. Use the pricing page or cost calculator with expected token volume.
  4. Set fallback and budget. Pick a primary model, a fallback, and a project budget before production traffic.

Fit score

Recommended candidates coding models

Start with the shortlist, then test real prompts and compare monthly cost before production routing in India.

AnthropicCatalog

Claude Fable 5 is a Mythos-class model from Anthropic, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, with reasoning support and...

$1.45 / 1M tokensInput$7.23 / 1M tokensOutput1MContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details
AnthropicCatalog

Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...

$0.145 / 1M tokensInput$0.723 / 1M tokensOutput200kContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details
AnthropicCatalog

Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...

$0.723 / 1M tokensInput$3.62 / 1M tokensOutput200kContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details
AnthropicCatalog

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...

$0.723 / 1M tokensInput$3.62 / 1M tokensOutput1MContext
Best forimage understanding, multimodal chat
RoutingConfigured
StreamingTool callingJSON modeVisionLong context
Platform curatedNextModel gateway catalog (Go origin)
View details

Comparison table

Compare the shortlist by price, provider, context, capability, and source.

Use this view when narrowing a production shortlist, building a fallback policy, or comparing model economics for India-based teams.

ModelProviderInputOutputContextCapabilitiesBest forLatencyStatusSource
Anthropic: Claude Fable 5anthropic/claude-fable-5Anthropic$1.45 / 1M tokens$7.23 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Haiku 4.5anthropic/claude-haiku-4.5Anthropic$0.145 / 1M tokens$0.723 / 1M tokens200k
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.5anthropic/claude-opus-4.5Anthropic$0.723 / 1M tokens$3.62 / 1M tokens200k
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.6anthropic/claude-opus-4.6Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Claude Opus 5anthropic/claude-opus-5Anthropic$0.723 / 1M tokens$3.62 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated
Anthropic: Claude Sonnet 4.5anthropic/claude-sonnet-4.5Anthropic$0.434 / 1M tokens$2.17 / 1M tokens1M
StreamingTool callingJSON modeVision
image understanding, multimodal chat1000-3000msCatalogPlatform curated

FAQ

Coding models FAQ

What makes a model good for coding agents?

Reliable tool calling, structured output, enough context for the repo slice you send, and instructions it actually follows. Token price alone is a poor proxy.

How should teams control coding-agent cost?

Cap budgets per project, watch output tokens, and send simple tasks to cheaper models. Escalate only when quality checks fail.

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