#![expect( clippy::disallowed_methods, reason = "integration tests stage fixtures with sync std::fs; test infrastructure, not Tokio-hot path" )] use fabro_test::{fabro_snapshot, test_context}; use httpmock::MockServer; #[test] fn help() { let context = test_context!(); let mut cmd = context.model(); cmd.arg("--help"); fabro_snapshot!(context.filters(), cmd, @" success: true exit_code: 0 ----- stdout ----- List and test LLM models Usage: fabro model [OPTIONS] [COMMAND] Commands: list List available models test Test model availability by sending a simple prompt help Print this message or the help of the given subcommand(s) Options: --json Output as JSON [env: FABRO_JSON=] --debug Enable DEBUG-level logging (default is INFO) [env: FABRO_DEBUG=] --no-upgrade-check Disable automatic upgrade check [env: FABRO_NO_UPGRADE_CHECK=true] --quiet Suppress non-essential output [env: FABRO_QUIET=] --verbose Enable verbose output [env: FABRO_VERBOSE=] -h, --help Print help ----- stderr ----- "); } #[test] fn bare() { let context = test_context!(); fabro_snapshot!(context.filters(), context.model(), @" success: true exit_code: 0 ----- stdout ----- MODEL PROVIDER ALIASES CONTEXT COST SPEED claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s claude-sonnet-4-5 anthropic 200k $3.0 / $15.0 50 tok/s claude-sonnet-4-6 anthropic sonnet, claude-sonnet 200k $3.0 / $15.0 50 tok/s claude-haiku-4-5 anthropic haiku, claude-haiku 200k $0.8 / $4.0 100 tok/s gpt-5.2 openai gpt5 1m $1.8 / $14.0 65 tok/s gpt-5-mini openai gpt5-mini 1m $0.2 / $2.0 70 tok/s gpt-5.2-codex openai 1m $1.8 / $14.0 100 tok/s gpt-5.3-codex openai codex 1m $1.8 / $14.0 100 tok/s gpt-5.3-codex-spark openai codex-spark 131k - / - 1000 tok/s gpt-5.4 openai gpt54, gpt-54 1m $2.5 / $15.0 70 tok/s gpt-5.5 openai gpt55, gpt-55 1m $5.0 / $30.0 70 tok/s gpt-5.5-pro openai gpt55-pro, gpt-55-pro 1m $30.0 / $180.0 20 tok/s gpt-5.4-pro openai gpt54-pro, gpt-54-pro 1m $30.0 / $180.0 20 tok/s gpt-5.4-mini openai gpt54-mini, gpt-54-mini 400k $0.8 / $4.5 140 tok/s gemini-3.1-pro-preview gemini gemini-pro 1m $2.0 / $12.0 85 tok/s gemini-3.1-pro-preview-customtools gemini gemini-customtools 1m $2.0 / $12.0 85 tok/s gemini-3-flash-preview gemini gemini-flash 1m $0.5 / $3.0 150 tok/s gemini-3.1-flash-lite-preview gemini gemini-flash-lite 1m $0.2 / $1.5 200 tok/s kimi-k2.5 kimi kimi 262k $0.6 / $3.0 50 tok/s glm-4.7 zai glm, glm4 203k $0.6 / $2.2 100 tok/s minimax-m2.5 minimax minimax 197k $0.3 / $1.2 45 tok/s mercury-2 inception mercury 131k $0.2 / $0.8 1000 tok/s ----- stderr ----- "); } #[test] fn list() { let context = test_context!(); let mut cmd = context.model(); cmd.arg("list"); fabro_snapshot!(context.filters(), cmd, @" success: true exit_code: 0 ----- stdout ----- MODEL PROVIDER ALIASES CONTEXT COST SPEED claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s claude-sonnet-4-5 anthropic 200k $3.0 / $15.0 50 tok/s claude-sonnet-4-6 anthropic sonnet, claude-sonnet 200k $3.0 / $15.0 50 tok/s claude-haiku-4-5 anthropic haiku, claude-haiku 200k $0.8 / $4.0 100 tok/s gpt-5.2 openai gpt5 1m $1.8 / $14.0 65 tok/s gpt-5-mini openai gpt5-mini 1m $0.2 / $2.0 70 tok/s gpt-5.2-codex openai 1m $1.8 / $14.0 100 tok/s gpt-5.3-codex openai codex 1m $1.8 / $14.0 100 tok/s gpt-5.3-codex-spark openai codex-spark 131k - / - 1000 tok/s gpt-5.4 openai gpt54, gpt-54 1m $2.5 / $15.0 70 tok/s gpt-5.5 openai gpt55, gpt-55 1m $5.0 / $30.0 70 tok/s gpt-5.5-pro openai gpt55-pro, gpt-55-pro 1m $30.0 / $180.0 20 tok/s gpt-5.4-pro openai gpt54-pro, gpt-54-pro 1m $30.0 / $180.0 20 tok/s gpt-5.4-mini openai gpt54-mini, gpt-54-mini 400k $0.8 / $4.5 140 tok/s gemini-3.1-pro-preview gemini gemini-pro 1m $2.0 / $12.0 85 tok/s gemini-3.1-pro-preview-customtools gemini gemini-customtools 1m $2.0 / $12.0 85 tok/s gemini-3-flash-preview gemini gemini-flash 1m $0.5 / $3.0 150 tok/s gemini-3.1-flash-lite-preview gemini gemini-flash-lite 1m $0.2 / $1.5 200 tok/s kimi-k2.5 kimi kimi 262k $0.6 / $3.0 50 tok/s glm-4.7 zai glm, glm4 203k $0.6 / $2.2 100 tok/s minimax-m2.5 minimax minimax 197k $0.3 / $1.2 45 tok/s mercury-2 inception mercury 131k $0.2 / $0.8 1000 tok/s ----- stderr ----- "); } #[test] fn list_provider() { let context = test_context!(); let mut cmd = context.model(); cmd.args(["list", "--provider", "anthropic"]); fabro_snapshot!(context.filters(), cmd, @" success: true exit_code: 0 ----- stdout ----- MODEL PROVIDER ALIASES CONTEXT COST SPEED claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s claude-sonnet-4-5 anthropic 200k $3.0 / $15.0 50 tok/s claude-sonnet-4-6 anthropic sonnet, claude-sonnet 200k $3.0 / $15.0 50 tok/s claude-haiku-4-5 anthropic haiku, claude-haiku 200k $0.8 / $4.0 100 tok/s ----- stderr ----- "); } #[test] fn list_query() { let context = test_context!(); let mut cmd = context.model(); cmd.args(["list", "--query", "opus"]); fabro_snapshot!(context.filters(), cmd, @" success: true exit_code: 0 ----- stdout ----- MODEL PROVIDER ALIASES CONTEXT COST SPEED claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s ----- stderr ----- "); } #[test] fn list_query_aliases() { let context = test_context!(); let mut cmd = context.model(); cmd.args(["list", "--query", "codex"]); fabro_snapshot!(context.filters(), cmd, @" success: true exit_code: 0 ----- stdout ----- MODEL PROVIDER ALIASES CONTEXT COST SPEED gpt-5.2-codex openai 1m $1.8 / $14.0 100 tok/s gpt-5.3-codex openai codex 1m $1.8 / $14.0 100 tok/s gpt-5.3-codex-spark openai codex-spark 131k - / - 1000 tok/s ----- stderr ----- "); } #[test] fn list_query_case_insensitive() { let context = test_context!(); let mut cmd = context.model(); cmd.args(["list", "--query", "OPUS"]); fabro_snapshot!(context.filters(), cmd, @" success: true exit_code: 0 ----- stdout ----- MODEL PROVIDER ALIASES CONTEXT COST SPEED claude-opus-4-7 anthropic opus, claude-opus 1m $5.0 / $25.0 25 tok/s claude-opus-4-6 anthropic 1m $5.0 / $25.0 25 tok/s ----- stderr ----- "); } #[test] fn list_invalid_provider_errors() { let context = test_context!(); let mut cmd = context.model(); cmd.args(["list", "--provider", "not-a-provider"]); fabro_snapshot!(context.filters(), cmd, @" success: false exit_code: 1 ----- stdout ----- ----- stderr ----- × unknown provider: not-a-provider "); } #[test] fn list_uses_configured_server_target_without_server_flag() { let context = test_context!(); let server = MockServer::start(); let mock = server.mock(|when, then| { when.method("GET"); then.status(200) .header("Content-Type", "application/json") .body( serde_json::json!({ "data": [{ "id": "remote-model", "display_name": "Remote Model", "provider": "openai", "family": "test", "aliases": ["remote"], "limits": { "context_window": 131_072, "max_output": 4096 }, "training": null, "knowledge_cutoff": null, "features": { "tools": true, "vision": false, "reasoning": false, "effort": false }, "costs": { "input_cost_per_mtok": 1.0, "output_cost_per_mtok": 2.0, "cache_input_cost_per_mtok": null }, "estimated_output_tps": 42.0, "default": false, "configured": false }], "meta": { "has_more": false } }) .to_string(), ); }); context.set_http_target(&server.base_url()); let mut cmd = context.model(); cmd.args(["list", "--json"]); let output = cmd.assert().success().get_output().stdout.clone(); let models: serde_json::Value = serde_json::from_slice(&output).expect("model list json should parse"); mock.assert(); assert_eq!(models.as_array().map(Vec::len), Some(1)); assert_eq!(models[0]["id"].as_str(), Some("remote-model")); } #[test] fn list_uses_fabro_config_for_machine_settings() { let context = test_context!(); let server = MockServer::start(); let mock = server.mock(|when, then| { when.method("GET"); then.status(200) .header("Content-Type", "application/json") .body( serde_json::json!({ "data": [{ "id": "remote-model", "display_name": "Remote Model", "provider": "openai", "family": "test", "aliases": ["remote"], "limits": { "context_window": 131_072, "max_output": 4096 }, "training": null, "knowledge_cutoff": null, "features": { "tools": true, "vision": false, "reasoning": false, "effort": false }, "costs": { "input_cost_per_mtok": 1.0, "output_cost_per_mtok": 2.0, "cache_input_cost_per_mtok": null }, "estimated_output_tps": 42.0, "default": false, "configured": false }], "meta": { "has_more": false } }) .to_string(), ); }); let config_dir = tempfile::tempdir().unwrap(); let config_path = config_dir.path().join("custom-settings.toml"); std::fs::write( &config_path, format!( "_version = 1\n\n[cli.target]\ntype = \"http\"\nurl = \"{}/api/v1\"\n", server.base_url() ), ) .unwrap(); let mut cmd = context.model(); cmd.args(["list", "--json"]); cmd.env("FABRO_CONFIG", &config_path); let output = cmd.assert().success().get_output().stdout.clone(); let models: serde_json::Value = serde_json::from_slice(&output).expect("model list json should parse"); mock.assert(); assert_eq!(models.as_array().map(Vec::len), Some(1)); assert_eq!(models[0]["id"].as_str(), Some("remote-model")); }